Friday, April 30, 2010
Sunday, March 7, 2010
Tuesday, February 16, 2010
Friday, February 12, 2010
Bayesian Network Repository
--------------------------------------------------------------------------------
Contents
About This Page
The datasets
Network formats and Utilities
Related Sites
--------------------------------------------------------------------------------
Mission
Our in intention is to construct a repository that will allow us empirical research within our community by facilitating (1)better reproducibility of results, and (2) better comparisons among competing approach. Both of these are required to measure progress on problems that are commonly agreed upon, such as inference and learning.
A motivation for this repository is outlined in "Challenge: Where is the impact of Bayesian networks in learning?" by N. Friedman, M. Goldszmidt, D. Heckerman, and S. Russell (IJCAI-97).
This will be achieved by several progressive steps:
Sharing domains. This would allow for reproduction of results, and also allow researchers in the community to run large scale empirical tests.
Sharing task specification. Sharing domains is not enough to compare algorithms. Thus, even if two papers examine inference in particular network, they might be answering different queries or assuming different evidence sets. The intent here is to store specific tasks. For example, in inference this might be a specific series of observations/queries. In learning, this might be a particular collection of training sets that have a particular pattern of missing data.
Sharing task evaluation. Even if two researchers examine the same task, they might use different measures to evaluate their algorithms. By sharing evaluation methods, we hope to allow for an objective comparison. In some cases such evaluation methods can be shared programs, such as a program the evaluates the quality of learned model by computing KL divergence to the "real" distribution. In other cases, such an evaluation method might be an agreed upon evaluation of performance, such as space requirements, number of floating point operations, etc.
Organized competitions. One of the dangers of empirical research is that the methods examined become overly tuned to specific evaluation domains. To avoid that danger, it is necessary to use "fresh" problems. The intention is to organize competitions that would address a specific problems, such as causal discovery, on unseen domains.
Plans for the future
Currently, this site contains several domains. The plan is to gradually add other components discussed above.
Please send suggestions and contributions to galel@cs.huji.ac.il.
Acknowledgements
Thanks to Fabio Cozman, Bruce D'Ambrosio, Moises Goldszmidt, David Heckerman, Othar Hansson, Daphne Koller, and Stuart Russell for discussions about the organization of this site. Thanks to John Binder, Jack Breese, David Heckerman, Uffe Kjaeruff, and Mark Peot, for contributing networks.
--------------------------------------------------------------------------------
galel@cs.huji.ac.il
Contents
About This Page
The datasets
Network formats and Utilities
Related Sites
--------------------------------------------------------------------------------
Mission
Our in intention is to construct a repository that will allow us empirical research within our community by facilitating (1)better reproducibility of results, and (2) better comparisons among competing approach. Both of these are required to measure progress on problems that are commonly agreed upon, such as inference and learning.
A motivation for this repository is outlined in "Challenge: Where is the impact of Bayesian networks in learning?" by N. Friedman, M. Goldszmidt, D. Heckerman, and S. Russell (IJCAI-97).
This will be achieved by several progressive steps:
Sharing domains. This would allow for reproduction of results, and also allow researchers in the community to run large scale empirical tests.
Sharing task specification. Sharing domains is not enough to compare algorithms. Thus, even if two papers examine inference in particular network, they might be answering different queries or assuming different evidence sets. The intent here is to store specific tasks. For example, in inference this might be a specific series of observations/queries. In learning, this might be a particular collection of training sets that have a particular pattern of missing data.
Sharing task evaluation. Even if two researchers examine the same task, they might use different measures to evaluate their algorithms. By sharing evaluation methods, we hope to allow for an objective comparison. In some cases such evaluation methods can be shared programs, such as a program the evaluates the quality of learned model by computing KL divergence to the "real" distribution. In other cases, such an evaluation method might be an agreed upon evaluation of performance, such as space requirements, number of floating point operations, etc.
Organized competitions. One of the dangers of empirical research is that the methods examined become overly tuned to specific evaluation domains. To avoid that danger, it is necessary to use "fresh" problems. The intention is to organize competitions that would address a specific problems, such as causal discovery, on unseen domains.
Plans for the future
Currently, this site contains several domains. The plan is to gradually add other components discussed above.
Please send suggestions and contributions to galel@cs.huji.ac.il.
Acknowledgements
Thanks to Fabio Cozman, Bruce D'Ambrosio, Moises Goldszmidt, David Heckerman, Othar Hansson, Daphne Koller, and Stuart Russell for discussions about the organization of this site. Thanks to John Binder, Jack Breese, David Heckerman, Uffe Kjaeruff, and Mark Peot, for contributing networks.
--------------------------------------------------------------------------------
galel@cs.huji.ac.il
Graphical Models -software tools
Working Group Neural Networks and Fuzzy Systems
Graphical Models
Software Tools back to the main page
Contents
Overview
BayesBuilder
Bayesian Knowledge Discoverer / Bayesware Discoverer
Bayes Net Toolbox
Belief Network Power Constructor
GeNIe / SMILE
Hugin
Netica
Pulcinella
Tetrad
WinMine / MSBN
Overview
On this page we briefly describe some software tools that support reasoning with graphical models and/or inducing them from a database of sample cases. Of course, we do not claim this list to be complete (definitely it is not). Nor does it represent a ranking of the tools, since they are ordered alphabetically. More extensive lists of probabilistic network tools have been compiled by
Russel Almond (an old list, which is not maintained anymore):
http://www.stat.washington.edu/almond/belief.html
Kevin Patrick Murphy:
http://www.cs.berkeley.edu/~murphyk/Bayes/bnsoft.html
and Google:
http://directory.google.com/Top/Computers/Artificial_Intelligence/Belief_Networks/Software/
The Bayesian Network Repository is also a valuable resource. It lists examples of Bayesian networks and datasets, from which they can be learned:
http://www.cs.huji.ac.il/labs/compbio/Repository/
The software we developed in connection with our book is available at:
http://fuzzy.cs.uni-magdeburg.de/books/gm/software.html
Tools for troubleshooting Microsoft products, which are based on Bayesian networks (but do not allow you to access them directly), can be found at
http://support.microsoft.com/support/tshoot/default.asp
back to the top
BayesBuilder
SNN, University of Nijmegen
PO Box 9101, 6500 HB Nijmegen, The Netherlands
http://www.mbfys.kun.nl/snn/Research/bayesbuilder/
BayesBuilder is a tool for (manually) constructing Bayesian networks and drawing inferences with them. It supports neither parameter nor structure learning of Bayesian networks. The graphical user interface of this program is written in Java and is easy to use. However, the program is available only for Windows, because the underlying inference engine is written in C++ and has only been compiled for Windows yet. BayesBuilder is free software.
back to the top
Bayesian Knowledge Discoverer / Bayesware Discoverer
Knowledge Media Institute / Department of Statistics
The Open University
Walton Hall, Milton Keynes MK7 6AA, United Kingdom
http://kmi.open.ac.uk/projects/bkd/
Bayesware Ltd.
http://bayesware.com/
The Bayesian Knowledge Discoverer is a software tool that can learn Bayesian networks from data (structure as well as parameters). The dataset to learn from may contain missing values, which are handled by an approach called "bound and collapse" that is based on probability intervals. The Bayesian Knowledge Discoverer is free software, but it has been succeeded by a commercial version, the Bayesware Discoverer. This program has a nice graphical user interface with some powerful visualization options. A 30 days trial version may be retrieved free of charge. Bayesware Discoverer is available for Windows, Unix and Macintosh.
back to the top
Bayes Net Toolbox
Kevin Patrick Murphy
Department of Computer Science, UC Berkeley
387 Soda Hall, Berkeley, CA 94720-1776, USA
http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html
The Bayes Net Toolbox is an extension for Matlab, a well-known and widely used mathematical software package. It supports several different algorithms for drawing inferences in Bayesian networks as well as several algorithms for learning the parameters and the structure of Bayesian networks from a dataset of sample cases. It does not have a graphical user interface of its own, but profits from the visualization capabilities of Matlab. The Bayes Net Toolbox is distributed under the Gnu Library General Public License and is available for all systems that can run Matlab, an installation of which is required.
back to the top
Belief Network Power Constructor
Jie Cheng
Dept. of Computing Science, University of Alberta
155 Athabasca Hall, Edmonton, Alberta, Canada T6G 2E1
http://www.cs.ualberta.ca/~jcheng/bnpc.htm
The Bayesian Network Power Constructor uses a three phase algorithm that is based on conditional independence tests to learn the structure of a Bayesian network from data. The conditional independence tests rely on mutual information, which is used to determine whether a (set of) node(s) can reduce or even block the information flow from one node to another. The program comes with a graphical user interface, though a much less advanced one than those of, for instance, HUGIN and Netica (see below). It does not support drawing inferences, but has the advantage that it is free software. It is available only for Windows.
back to the top
GeNIe / SMILE
Decision Systems Laboratory, University of Pittsburgh
B212 SLIS Building, 135 North Bellefield Avenue, Pittsburgh, PA 15260, USA
http://www2.sis.pitt.edu/~genie/
SMILE (Structural Modeling, Inference and Learning Engine) is a library of functions for building Bayesian networks and drawing inferences with them. It does support neither parameter nor structural learning of Bayesian networks. GeNIe (Graphical Network Interface) is a graphical user interface for SMILE, that makes the functions of SMILE easily accessible. While SMILE is platform independent, GeNIe is available only for Windows, since it relies heavily on the Microsoft Foundation classes. Both packages are distributed free of charge.
back to the top
Hugin
Hugin Expert A/S
Niels Jernes Vej 10, 9220 Aalborg, Denmark
http://www.hugin.com
Hugin is one of the oldest and best-known tools for Bayesian network construction and inference. It comes with an easy to use graphical user interface, but also has an API (application programmers interface) for several programming languages, so that the inference engine can be used in other programs. It supports estimating the parameters of a Bayesian network from a dataset of sample cases. In a recent version it has also been extended by a learning algorithm for the structure of a Bayesian network, which is based on conditional independence tests. Hugin is a commercial tool, but a demo version with restricted capabilities may be retrieved free of charge. Hugin is available for Windows and Solaris (Sun Unix).
back to the top
Netica
Norsys Software Corp.
2315 Dunbar Street, Vancouver, BC, Canada V6R 3N1
http://www.norsys.com
Like Hugin, Netica is a commercial tool with an advanced graphical user interface. It supports Bayesian network construction and inference and also comprises an API (application programmers interface) for C++, so that the inference engine may be used in other programs. Netica offers quantitative network learning (known structure, parameter estimation) from a dataset of sample cases, which may contain missing values. It does not support structural learning. A version of Netica with restricted capabilities may be retrieved free of charge, but the price of a full version is also moderate. Netica is available for Windows and Macintosh.
back to the top
Pulcinella
IRIDA, Université Libre de Bruxelles
50, Av. F. Roosevelt, CP 194/6, B-1050 Brussels, Belgium
http://iridia.ulb.ac.be/pulcinella/Welcome.html
Pulcinella is more general than the other programs listed on this page, as it is based on the framework of valuation systems [Shenoy 1992a]. Pulcinella supports reasoning by propagating uncertainty with local computations w.r.t. different uncertainty calculi, but does not support learning graphical models from a dataset of sample cases in any way. The current version of Pulcinella does not have a graphical user interface, but an outdated version of such an interface may be retrieved for Solaris (Sun Unix). Pulcinella is available for Solaris (Sun Unix) and Macintosh, but requires a Common Lisp system.
back to the top
Tetrad
Tetrad Project, Department of Philosophy
Carnegie Mellon University, Pittsburgh, PA, USA
http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.htm
Tetrad is based on the algorithms developed in [Spirtes et al 1993], i.e. on conditional independence test approaches to learn Bayesian networks from data, and, of course, subsequent research in this direction. It can learn the structure as well as the parameters of a Bayesian network from a dataset of sample cases, but does not support drawing inferences. Currently the program is being ported to Java (Tetrad IV). Older versions are available for MSDOS (Tetrad II) and Windows (Tetrad III). Tetrad II is commercial, but available at a moderate fee. Free beta versions are available of Tetrad III and Tetrad IV.
back to the top
WinMine / MSBN
Machine Learning and Statistics Group
Microsoft Research, One Microsoft Way, Redmond, WA 98052-6399, USA
http://research.microsoft.com/~dmax/WinMine/tooldoc.htm
WinMine is a toolkit, i.e. a set of programs for different tasks, rather than an integrated program. Most programs in this toolkit are command line driven, but there is a graphical user interface for the data converter and a network visualization program. WinMine learns the structure and the parameters of Bayesian networks from data and uses decision trees to represent the conditional distributions. It does not support drawing inferences. However, Microsoft Research also offers MSBN (Microsoft Bayesian Networks), a tool for (manually) building Bayesian networks and drawing inferences with them, MSBN comes with a graphical user interface. Both programs, WinMine as well as MSBN, are available for Windows only.
back to the top
© 2002 Christian Borgelt
Last modified: Fri Oct 25 11:05:52 MEST 2002
Graphical Models
Software Tools back to the main page
Contents
Overview
BayesBuilder
Bayesian Knowledge Discoverer / Bayesware Discoverer
Bayes Net Toolbox
Belief Network Power Constructor
GeNIe / SMILE
Hugin
Netica
Pulcinella
Tetrad
WinMine / MSBN
Overview
On this page we briefly describe some software tools that support reasoning with graphical models and/or inducing them from a database of sample cases. Of course, we do not claim this list to be complete (definitely it is not). Nor does it represent a ranking of the tools, since they are ordered alphabetically. More extensive lists of probabilistic network tools have been compiled by
Russel Almond (an old list, which is not maintained anymore):
http://www.stat.washington.edu/almond/belief.html
Kevin Patrick Murphy:
http://www.cs.berkeley.edu/~murphyk/Bayes/bnsoft.html
and Google:
http://directory.google.com/Top/Computers/Artificial_Intelligence/Belief_Networks/Software/
The Bayesian Network Repository is also a valuable resource. It lists examples of Bayesian networks and datasets, from which they can be learned:
http://www.cs.huji.ac.il/labs/compbio/Repository/
The software we developed in connection with our book is available at:
http://fuzzy.cs.uni-magdeburg.de/books/gm/software.html
Tools for troubleshooting Microsoft products, which are based on Bayesian networks (but do not allow you to access them directly), can be found at
http://support.microsoft.com/support/tshoot/default.asp
back to the top
BayesBuilder
SNN, University of Nijmegen
PO Box 9101, 6500 HB Nijmegen, The Netherlands
http://www.mbfys.kun.nl/snn/Research/bayesbuilder/
BayesBuilder is a tool for (manually) constructing Bayesian networks and drawing inferences with them. It supports neither parameter nor structure learning of Bayesian networks. The graphical user interface of this program is written in Java and is easy to use. However, the program is available only for Windows, because the underlying inference engine is written in C++ and has only been compiled for Windows yet. BayesBuilder is free software.
back to the top
Bayesian Knowledge Discoverer / Bayesware Discoverer
Knowledge Media Institute / Department of Statistics
The Open University
Walton Hall, Milton Keynes MK7 6AA, United Kingdom
http://kmi.open.ac.uk/projects/bkd/
Bayesware Ltd.
http://bayesware.com/
The Bayesian Knowledge Discoverer is a software tool that can learn Bayesian networks from data (structure as well as parameters). The dataset to learn from may contain missing values, which are handled by an approach called "bound and collapse" that is based on probability intervals. The Bayesian Knowledge Discoverer is free software, but it has been succeeded by a commercial version, the Bayesware Discoverer. This program has a nice graphical user interface with some powerful visualization options. A 30 days trial version may be retrieved free of charge. Bayesware Discoverer is available for Windows, Unix and Macintosh.
back to the top
Bayes Net Toolbox
Kevin Patrick Murphy
Department of Computer Science, UC Berkeley
387 Soda Hall, Berkeley, CA 94720-1776, USA
http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html
The Bayes Net Toolbox is an extension for Matlab, a well-known and widely used mathematical software package. It supports several different algorithms for drawing inferences in Bayesian networks as well as several algorithms for learning the parameters and the structure of Bayesian networks from a dataset of sample cases. It does not have a graphical user interface of its own, but profits from the visualization capabilities of Matlab. The Bayes Net Toolbox is distributed under the Gnu Library General Public License and is available for all systems that can run Matlab, an installation of which is required.
back to the top
Belief Network Power Constructor
Jie Cheng
Dept. of Computing Science, University of Alberta
155 Athabasca Hall, Edmonton, Alberta, Canada T6G 2E1
http://www.cs.ualberta.ca/~jcheng/bnpc.htm
The Bayesian Network Power Constructor uses a three phase algorithm that is based on conditional independence tests to learn the structure of a Bayesian network from data. The conditional independence tests rely on mutual information, which is used to determine whether a (set of) node(s) can reduce or even block the information flow from one node to another. The program comes with a graphical user interface, though a much less advanced one than those of, for instance, HUGIN and Netica (see below). It does not support drawing inferences, but has the advantage that it is free software. It is available only for Windows.
back to the top
GeNIe / SMILE
Decision Systems Laboratory, University of Pittsburgh
B212 SLIS Building, 135 North Bellefield Avenue, Pittsburgh, PA 15260, USA
http://www2.sis.pitt.edu/~genie/
SMILE (Structural Modeling, Inference and Learning Engine) is a library of functions for building Bayesian networks and drawing inferences with them. It does support neither parameter nor structural learning of Bayesian networks. GeNIe (Graphical Network Interface) is a graphical user interface for SMILE, that makes the functions of SMILE easily accessible. While SMILE is platform independent, GeNIe is available only for Windows, since it relies heavily on the Microsoft Foundation classes. Both packages are distributed free of charge.
back to the top
Hugin
Hugin Expert A/S
Niels Jernes Vej 10, 9220 Aalborg, Denmark
http://www.hugin.com
Hugin is one of the oldest and best-known tools for Bayesian network construction and inference. It comes with an easy to use graphical user interface, but also has an API (application programmers interface) for several programming languages, so that the inference engine can be used in other programs. It supports estimating the parameters of a Bayesian network from a dataset of sample cases. In a recent version it has also been extended by a learning algorithm for the structure of a Bayesian network, which is based on conditional independence tests. Hugin is a commercial tool, but a demo version with restricted capabilities may be retrieved free of charge. Hugin is available for Windows and Solaris (Sun Unix).
back to the top
Netica
Norsys Software Corp.
2315 Dunbar Street, Vancouver, BC, Canada V6R 3N1
http://www.norsys.com
Like Hugin, Netica is a commercial tool with an advanced graphical user interface. It supports Bayesian network construction and inference and also comprises an API (application programmers interface) for C++, so that the inference engine may be used in other programs. Netica offers quantitative network learning (known structure, parameter estimation) from a dataset of sample cases, which may contain missing values. It does not support structural learning. A version of Netica with restricted capabilities may be retrieved free of charge, but the price of a full version is also moderate. Netica is available for Windows and Macintosh.
back to the top
Pulcinella
IRIDA, Université Libre de Bruxelles
50, Av. F. Roosevelt, CP 194/6, B-1050 Brussels, Belgium
http://iridia.ulb.ac.be/pulcinella/Welcome.html
Pulcinella is more general than the other programs listed on this page, as it is based on the framework of valuation systems [Shenoy 1992a]. Pulcinella supports reasoning by propagating uncertainty with local computations w.r.t. different uncertainty calculi, but does not support learning graphical models from a dataset of sample cases in any way. The current version of Pulcinella does not have a graphical user interface, but an outdated version of such an interface may be retrieved for Solaris (Sun Unix). Pulcinella is available for Solaris (Sun Unix) and Macintosh, but requires a Common Lisp system.
back to the top
Tetrad
Tetrad Project, Department of Philosophy
Carnegie Mellon University, Pittsburgh, PA, USA
http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.htm
Tetrad is based on the algorithms developed in [Spirtes et al 1993], i.e. on conditional independence test approaches to learn Bayesian networks from data, and, of course, subsequent research in this direction. It can learn the structure as well as the parameters of a Bayesian network from a dataset of sample cases, but does not support drawing inferences. Currently the program is being ported to Java (Tetrad IV). Older versions are available for MSDOS (Tetrad II) and Windows (Tetrad III). Tetrad II is commercial, but available at a moderate fee. Free beta versions are available of Tetrad III and Tetrad IV.
back to the top
WinMine / MSBN
Machine Learning and Statistics Group
Microsoft Research, One Microsoft Way, Redmond, WA 98052-6399, USA
http://research.microsoft.com/~dmax/WinMine/tooldoc.htm
WinMine is a toolkit, i.e. a set of programs for different tasks, rather than an integrated program. Most programs in this toolkit are command line driven, but there is a graphical user interface for the data converter and a network visualization program. WinMine learns the structure and the parameters of Bayesian networks from data and uses decision trees to represent the conditional distributions. It does not support drawing inferences. However, Microsoft Research also offers MSBN (Microsoft Bayesian Networks), a tool for (manually) building Bayesian networks and drawing inferences with them, MSBN comes with a graphical user interface. Both programs, WinMine as well as MSBN, are available for Windows only.
back to the top
© 2002 Christian Borgelt
Last modified: Fri Oct 25 11:05:52 MEST 2002
Wednesday, January 27, 2010
Robert Burns 罗伯特·彭斯
Robert Burns 罗伯特·彭斯
Robert Burns
Born in Alloway, Scotland, on January 25, 1759, Robert Burns was the first of William and Agnes Burnes' seven children. His father, a tenant farmer, educated his children at home. Burns also attended one year of mathematics schooling and, between 1765 and 1768, he attended an "adventure" school established by his father and John Murdock. His father died in bankruptcy in 1784, and Burns and his brother Gilbert took over farm. This hard labor later contributed to the heart trouble that Burns' suffered as an adult.
At the age of fifteen, he fell in love and shortly thereafter he wrote his first poem. As a young man, Burns pursued both love and poetry with uncommon zeal. In 1785, he fathered the first of his fourteen children. His biographer, DeLancey Ferguson, had said, "it was not so much that he was conspicuously sinful as that he sinned conspicuously." Between 1784 and 1785, Burns also wrote many of the poems collected in his first book, Poems, Chiefly in the Scottish Dialect, which was printed in 1786 and paid for by subscriptions. This collection was an immediate success and Burns was celebrated throughout England and Scotland as a great "peasant-poet."
In 1788, he and his wife, Jean Armour, settled in Ellisland, where Burns was given a commission as an excise officer. He also began to assist James Johnson in collecting folk songs for an anthology entitled The Scots Musical Museum. Burns' spent the final twelve years of his life editing and imitating traditional folk songs for this volume and for Select Collection of Original Scottish Airs. These volumes were essential in preserving parts of Scotland's cultural heritage and include such well-known songs as "My Luve is Like a Red Red Rose" and "Auld Land Syne." Robert Burns died from heart disease at the age of thirty-seven. On the day of his death, Jean Armour gave birth to his last son, Maxwell.
Most of Burns' poems were written in Scots. They document and celebrate traditional Scottish culture, expressions of farm life, and class and religious distinctions. Burns wrote in a variety of forms: epistles to friends, ballads, and songs. His best-known poem is the mock-heroic Tam o' Shanter. He is also well known for the over three hundred songs he wrote which celebrate love, friendship, work, and drink with often hilarious and tender sympathy. Even today, he is often referred to as the National Bard of Scotland.
外部链接：彭斯官方网：http://www.robertburns.org/
英国诗人 。1759年1月25日生于苏格兰艾尔郡阿洛韦镇的一个佃农家庭，1796年7月21日卒于邓弗里斯。自幼家境贫寒，未受过正规教育，靠自学获得多方面的知识。最优秀的诗歌作品产生于1785～1790年 ，收集在诗集《主要以苏格兰方言而写的诗》中。诗集体现了诗人一反当时英国诗坛的新古典主义诗风，从地方生活和民间文学中汲取营养，为诗歌创作带来了新鲜的活力，形成了他诗歌创作的基本特色。以虔诚的感情歌颂大自然及乡村生活；以入木三分的犀利言辞讽刺教会及日常生活中人们的虚伪。诗集使彭斯一举成名，被称为天才的农夫。后应邀到爱丁堡，出入于上流社会的显贵中间。但发现自己高傲的天性和激进思想与上流社会格格不入，乃返回故乡务农。一度到苏格兰北部高原地区游历，后来当了税务官，一边任职一边创作。
彭斯的诗歌作品多使用苏格兰方言，并多为抒情短诗，如歌颂爱情的名篇《我的爱人像朵红红的玫瑰》和抒发爱国热情的《苏格兰人》等。他还创作了不少讽刺诗（如《威利长老的祈祷》），诗札（如《致拉布雷克书》）和叙事诗（如《两只狗》和《快活的乞丐》）。作品表达了平民阶级的思想感情，同情下层人民疾苦，同时以健康、自然的方式体现了追求“美酒、女人和歌”的快乐主义人生哲学。彭斯富有敏锐的幽默感。对苏格兰乡村生活的生动描写使他的诗歌作品具有民族特色和艺术魅力。
除诗歌创作外，彭斯还收集整理大量的苏格兰民间歌谣，编辑出版了6卷本的《苏格兰音乐总汇》和8卷本的《原始的苏格兰歌曲选集》。其中《友谊地久天长》不仅享誉苏格兰，而且闻名世界。
在地球的各个角落，在亲朋的离别或是会议的告别仪式，人们以各种不同语言齐唱《友谊地久天长》(Auld Lang Syne，又名《骊歌》)，朋友们紧紧挽着手，歌唱永不相忘的友谊。它驱走了人们离别的哀愁，使人们满怀激情各奔前程。这首家喻户晓的苏格兰民歌的词作者，即是著名的民族诗人罗伯特·彭斯(Robert Burns，1759－1796)。
彭斯出生在一个贫苦农民家庭，以租地耕种为生。幼时在苏格兰家乡附近上小学。不久校长离去，父亲请老师来家教学。老师认为彭斯兄弟不比年长的同学差。父亲晚上教他们文法及神学。12岁时彭斯二兄弟又轮流去离家四英里的村落上学，14岁在学习英语之余，开始学习法文。
彭斯15岁时成为父亲身边主要的劳动力，驾驭马匹在土墩及洼地上耕作。劳动极为艰辛。虽数次更换土地租耕，因土地贫瘠，收获仍然不佳。劳动之余，彭斯爱读苏格兰诗人申斯通(Shenstone，1714-1793)、蒲柏（Pope，1688-1744）及弗格森（Fergusson 1750-1774）的作品，也浏览苏格兰小说家麦肯齐(MacKenzie，1745-1831)的书。他希望能成为苏格兰艾尔郡(Ayrshire)的诗人，歌唱故乡的山河。
1784年他的父亲去世，全家迁去莫斯吉尔（Mossgiel)，耕作收获并无好转。幸而他的主要用苏格兰方言写的诗集得以出版并迅即获得成功，爱丁堡的出版商又很快为之再版。编辑文学杂志的麦肯齐在评论中称赞这位庄稼汉是位诗歌天才。
于是在爱丁堡，彭斯穿起深色大衣、浅背心、皱边的衬衫，足登鹿皮鞋或长靴，过起出入文学集会及酒馆的双重生活。
彭斯在爱丁堡生活及游历苏格兰一段时期后，仍回乡务农。1788年他考取税务局职员，除了在农田干活外，还要每周在马背上驰骋200英里去上班。执法时，他不放过大鱼，但对贫穷者则手下留情。他为了全力做好税务局的工作，1791年放弃农活迁往邓弗里斯（Dumfries），在那儿度过了他的最后的岁月。
彭斯写了大量的抒情诗，还写讽刺诗及叙事诗。他也喜爱歌曲，有敏锐的音乐耳朵，对节奏有良好的反应。他把最后十年的精力，主要放在为二个丛刊的整理及收集民歌上，使濒临失传的三百多首民歌得以保存下来。
1796年他患了风湿性关节炎及心脏病，于同年7月21日英年早逝。前来送葬的多达二万人。
当年彭斯出生并度过了七年童年的茅屋，位于艾尔郡的阿洛韦镇(Alloway)，现由彭斯纪念碑信托基金机构管理。与茅屋相连接的红瓦顶、前面为长廊及花园的博物馆，为信托机构理事会于1920年所扩建。1994年该理事会重铺稻草屋顶，再建18世纪的菜园及石堤。这就是现在世界著名的彭斯茅舍。
彭斯雕像位于艾尔市中心。从市中心附近坐开往杜恩河老桥(Auld Brig O’Doon)的公共汽车，在终点老桥前一站下车，即见到茅草顶的白色平房，木制的门窗是深棕色的。门上方的黑色纪念板上写着“彭斯茅舍”，接着是“罗伯特·彭斯—艾尔郡诗人”及他的生卒年月。进门后先是谷仓，然后是牛棚及马厩。依稀传来牲畜的叫声，蜡制的耕牛旁还有几只母鸡在啄食谷粒。起居室中以蜡像布置一家人当时融洽的情景。父亲在烛光下读《圣经》，母亲抱着妹妹坐在对面，弟弟坐在一边，彭斯则光着脚站在一旁专心听讲。一个小妹妹躺在摇篮里。厨房里熏黑的炉灶还生着火，彭斯出生的床即在厨房内。布置一如当年。幼年时母亲在这里教孩子们唱苏格兰民歌，姨母则介绍给他们大量有关鬼怪神仙的故事和歌曲。“彭斯茅屋”对崇拜彭斯的人来说是圣地，但也是当年贫苦农家家居生活的写照。
彭斯长期生活在农村，从事繁重的农活。地主的剥削，加上土地的贫瘠，欠收、负债、迁居……使他常常过着没有温饱的生活。但他热爱生活，对劳动人民有深厚的感情。在《两只狗》这首诗里，通过两只分属贫富人家的狗之间的对话，描绘了地主家的骄奢淫逸。贫穷的佃户虽然耕作及劳动辛苦，但同欢共乐聚在一起。而这两家的狗能够融洽相处，成为人类不公平生活的鲜明对照。
诗人也理解农民对牲口的深厚感情。他在《新年早晨老农向老马麦琪致辞》一诗中，回顾了老马一生的辛劳后，写道：“我将在留下的麦地上面，把你的缰绳系好，不用费大力气，你就在那边舒舒畅畅吃个饱。”
与彭斯茅屋相通连的彭斯博物馆，收藏了彭斯珍贵的手稿、他的包括早期版本的作品、有关的画像等，有些收藏品来自美国、加拿大甚至南非。
大展览室介绍他一生的劳动、写作和生活。以图片的形式，配合他的诗句、信件或日记，生动地叙述了他当年的经历。这里还展出了他的怀表、记事本、墨水瓶、鼻烟壶，两把柄上刻有“R.B.”的手枪，以及当税务员测酒用的长棒，也展有1786年版的主要用苏格兰方言写的诗集。笔者自然记得寻找《友谊地久天长》的原稿，它原来出自彭斯于1788年写给友人的一封信中。
第二室展出数幅著名的油画。《羊杂宴》描绘彭斯夫妇款待客人的场景。彭斯喜欢这种热闹场面。另有一组四幅的版画，描绘他的作品《汤姆·奥桑特的故事》(Tam o’Shanter)，彭斯这部根据民间传说写的长诗，讲的是汤姆深夜回家途中遇鬼的故事。他去除了传说中迷信的成分，以喜剧形式讲魔法，寓有深意。同时，它也把儿时听到的传说，与故乡阿罗韦他幼时熟悉的界标、陈旧的教堂、古老的石桥和石冢等联系起来，具有沧桑感和神秘感。
离开这里步行一里，抵达老杜恩河桥，彭斯纪念碑即位于附近的山丘上。这座希腊式建筑由爱丁堡著名建筑师设计，1823年完成，耗资3247镑。登上这座台式纪念碑，可眺望杜恩河及卡里克山(Carrick Hill)的优美景色。纪念碑的基座建有展览室，展出15种外国文字的彭斯著作。在附近花园里，还建有一个雕像室，内有三座《汤姆·奥桑特的故事》中的人物雕像，真人大小，造型风趣。
归途中于老杜恩河老桥公共汽车站，见到一家大百货公司，里面的多种商品以彭斯命名。如果时间合适(回到艾尔的公共汽车每小时一班)，还可看一看介绍彭斯的记录影片。
在他住过的基尔马诺克（Kilmarnock）及邓弗里斯也建有彭斯博物馆、雕像或纪念碑，欧文（lrving）也有他的雕像。甚至远在加拿大和澳大利亚，也有他的纪念碑。位于苏格兰首府爱丁堡的“三作家博物馆”介绍了彭斯、司各特(Walter Scott，1771-1832)及斯蒂文森(Robert L.Stevenson， 1850-1894)的生平，也值得一去。
在彭斯的故乡苏格兰，有数千个彭斯联谊会，苏格兰各地每年都庆祝他的生日。
如此广受故乡人民爱戴的诗人，在世界上也不多见。因为除了长期生活在农村并写出描绘故乡及朴直人民的诗歌外，身受民族压迫的他十分热爱苏格兰，并热情歌颂民主及自由。
在彭斯的青年时代，先后爆发了美国独立战争和法国大革命。他关心世界政治及苏格兰祖国的命运。他写的《华盛顿将军生辰颂》，赞扬美国人民的独立斗争。在法国大革命的影响下，他写了《自由树》和《苏格兰人》两首著名长诗。《自由树》声言有了法兰西这棵自由树，人类将变得平等，世界将获得和平。《苏格兰人》重温历史，以颂扬早年民族英雄华莱士等人的事迹来激励人民：
谁愿为苏格兰国君和法律，
奋力把自由之剑拔出？
生为自由人，死为自由魂，
让他跟我前进！
彭斯是人民的诗人，也是为自由而斗争的战士。这颗明亮的星，永远闪耀在苏格兰的上空，也永远闪耀在爱好和平与友谊的人们心中。
【作品选译】
一朵红红的玫瑰 【英文朗诵：下载地址】
啊，我的爱人象朵红红的玫瑰，
六月里迎风初开，
啊，我的爱人象支甜甜的曲子，
奏得合拍又和谐。
我的好姑娘，多么美丽的人儿！
请看我，多么深挚的爱情！
亲爱的，我永远爱你，
纵使大海干涸水流尽。
纵使大海千涸水流尽，
太阳将岩石烧作灰尘，
亲爱的，我永远爱你，
只要我一息犹存。
珍重吧，我唯一的爱人，
珍重吧，让我们暂时别离，
但我定要回来，
哪怕千里万里！
王佐良译
往昔的时光
老朋友哪能遗忘，
哪能不放在心上？
老朋友哪能遗忘，
还有往昔的时光？
为了往昔的时光，老朋友，
为了往昔的时光，
再干一杯友情的酒，
为了往昔的时光，
你来痛饮一大杯，
我也买酒来相陪。
干一杯友情的酒又何妨？
为了往昔的时光。
我们曾邀游山岗，
到处将野花拜访。
但以后走上疲惫的旅程，
逝去了往昔的时光！
我们曾赤脚瞠过河流，
水声笑语里将时间忘。
如今大海的怒涛把我们隔开，
逝去了往昔的时光！
忠实的老友，伸出你的手，
让我们握手聚一堂，
再来痛饮—杯欢乐酒，
为了往昔的时光！
王佐良译
给我开门，哦！
曲调：轻轻地开门
哦，开门，纵使你对戬无情，
也表一点怜悯，哦。
你虽变了心，我仍忠于糟．
哦，给我开门，哦。
风吹我苍白的双颊，好冷！
但冷不过你对我的心，哦．
冰霜使我心血凝冻，
也没你给我的痛深，哦。
残月沉落白水中，
时间也随我沉落，哦。
假朋友，变心人，永别不再逢！
我决不再采烦渎，哦。
她把门儿大敞开，
见了平地上苍白的尸体，哦，
只喊了一声“爱’就倒在尘埃，
从此再也不起，哦。
王佐良译
走过麦田来
（合唱）啊，珍尼是可怜的人儿，
珍尼哭得悲哀。
她拖着长裙，
走过麦田来。
可怜的人儿，走过麦田来，
走过麦田来，
她拖着长裙
走过麦田来。
如果一个他碰见一个她，
走过麦田来，
如果一个他吻了一个她，
她何必哭起来？
如果一个他碰见一个她
走过山间小道，
如果一个他吻了一个她，
别人哪用知道！
（合唱）啊，珍尼是可怜的人儿，
珍尼哭得悲哀。
她拖着长裙，
走过麦田来。
王佐良译
如果你站在冷风里
呵，如果你站在冷风里，
一人在草地，在草地，
我的小屋会挡住凶恶的风，
保护你，保护你。
如果灾难象风暴袭来，
落在你头上，你头上，
我将用胸脯温暖你，
一切同享，一切同当。
如果我站在可怕的荒野，
天黑又把路迷，把路迷，
就是沙漠也变成天堂，
只要有你，只要有你。
如果我是地球的君王，
宝座我们共有，我们共有，
我的王冠上有一粒最亮的珍珠——
它是我的王后，我的王后。
王佐良译
选自《彭斯诗选》，人民文学出版社(1959)
苏格兰人①
跟华莱士流过血的苏格兰人，
随布鲁斯作过战的苏格兰人，
起来！倒在血泊里也成——
要不就夺取胜利！
时刻已到，决战已近，
前线的军情吃紧，
骄横的爱德华在统兵入侵——
带来锁链，带来奴役！
谁愿卖国求荣？
谁愿爬进懦夫的坟茔？
谁卑鄙到宁做奴隶偷生？——
让他走，让他逃避！
谁愿将苏格兰国王和法律保护，
拔出自由之剑来痛击、猛舞？
谁愿生作自由人，死作自由魂？——
让他来，跟我出击！
凭被压迫者的苦难采起誓，
凭你们受奴役的子孙来起誓，
我们决心流血到死——
但他们必须自由！
打倒骄横的篡位者！
死一个敌人，少一个暴君！
多一次攻击，添一分自由！
动手——要不就断头！
袁可嘉译
①这是彭斯所作爱国诗中最著名的一首，写的是苏格兰
国王罗伯特·布鲁斯在大破英国侵略军的班诺克本一役
(1314)之前向部队所作的号召。首先发表在1794年6月的
《纪事晨报》。
诗中所提的华莱士是一位十三世纪的英格兰民族英雄，
也曾大败英军。但后来为奸人出卖，被执处死。爱德华指
英王爱德华二世。
彭斯一直念念不忘为苏格兰民族独立而斗争的志士，
写此诗时爱国热情尤其澎湃。不仅如此，他还借古讽今，
曾经明白写信告诉朋友说：启发他写这首诗的不止是古代
那场“光荣的争取自由的斗争”，而还有“在时间上却不
是那么遥远的同类性质的斗争”，即法国大革命，当时正
方兴未艾，在苏格兰的彼岸如火如荼地展开。
我的心儿在高原①
我的心儿在高原，我的心不在这儿，
我的心儿在高原，迫遂着鹿儿。
追逐着野鹿，跟踪着獐儿；
我的心儿在高原，不管我上哪儿，
别了啊高原，别了啊北国，
英雄的家乡，可敬的故国，
不管我上哪儿漂荡，我上哪儿遨游，
我永远爱着高原的山丘。
别了啊，高耸的积雪的山岳，
别了啊，山下的溪壑和翠谷，
别了啊，森林和枝檀纵横的树林，
别了啊，急川和洪流的轰鸣，
我的心儿在高原，我的心不在这儿，
我的心儿在高原，追逐着鹿儿，
追逐着野鹿，跟踪着獐儿，
我的心儿在高原，不管我上哪儿。
袁可嘉译
①苏格兰北部地区。
Robert Burns
Born in Alloway, Scotland, on January 25, 1759, Robert Burns was the first of William and Agnes Burnes' seven children. His father, a tenant farmer, educated his children at home. Burns also attended one year of mathematics schooling and, between 1765 and 1768, he attended an "adventure" school established by his father and John Murdock. His father died in bankruptcy in 1784, and Burns and his brother Gilbert took over farm. This hard labor later contributed to the heart trouble that Burns' suffered as an adult.
At the age of fifteen, he fell in love and shortly thereafter he wrote his first poem. As a young man, Burns pursued both love and poetry with uncommon zeal. In 1785, he fathered the first of his fourteen children. His biographer, DeLancey Ferguson, had said, "it was not so much that he was conspicuously sinful as that he sinned conspicuously." Between 1784 and 1785, Burns also wrote many of the poems collected in his first book, Poems, Chiefly in the Scottish Dialect, which was printed in 1786 and paid for by subscriptions. This collection was an immediate success and Burns was celebrated throughout England and Scotland as a great "peasant-poet."
In 1788, he and his wife, Jean Armour, settled in Ellisland, where Burns was given a commission as an excise officer. He also began to assist James Johnson in collecting folk songs for an anthology entitled The Scots Musical Museum. Burns' spent the final twelve years of his life editing and imitating traditional folk songs for this volume and for Select Collection of Original Scottish Airs. These volumes were essential in preserving parts of Scotland's cultural heritage and include such well-known songs as "My Luve is Like a Red Red Rose" and "Auld Land Syne." Robert Burns died from heart disease at the age of thirty-seven. On the day of his death, Jean Armour gave birth to his last son, Maxwell.
Most of Burns' poems were written in Scots. They document and celebrate traditional Scottish culture, expressions of farm life, and class and religious distinctions. Burns wrote in a variety of forms: epistles to friends, ballads, and songs. His best-known poem is the mock-heroic Tam o' Shanter. He is also well known for the over three hundred songs he wrote which celebrate love, friendship, work, and drink with often hilarious and tender sympathy. Even today, he is often referred to as the National Bard of Scotland.
外部链接：彭斯官方网：http://www.robertburns.org/
英国诗人 。1759年1月25日生于苏格兰艾尔郡阿洛韦镇的一个佃农家庭，1796年7月21日卒于邓弗里斯。自幼家境贫寒，未受过正规教育，靠自学获得多方面的知识。最优秀的诗歌作品产生于1785～1790年 ，收集在诗集《主要以苏格兰方言而写的诗》中。诗集体现了诗人一反当时英国诗坛的新古典主义诗风，从地方生活和民间文学中汲取营养，为诗歌创作带来了新鲜的活力，形成了他诗歌创作的基本特色。以虔诚的感情歌颂大自然及乡村生活；以入木三分的犀利言辞讽刺教会及日常生活中人们的虚伪。诗集使彭斯一举成名，被称为天才的农夫。后应邀到爱丁堡，出入于上流社会的显贵中间。但发现自己高傲的天性和激进思想与上流社会格格不入，乃返回故乡务农。一度到苏格兰北部高原地区游历，后来当了税务官，一边任职一边创作。
彭斯的诗歌作品多使用苏格兰方言，并多为抒情短诗，如歌颂爱情的名篇《我的爱人像朵红红的玫瑰》和抒发爱国热情的《苏格兰人》等。他还创作了不少讽刺诗（如《威利长老的祈祷》），诗札（如《致拉布雷克书》）和叙事诗（如《两只狗》和《快活的乞丐》）。作品表达了平民阶级的思想感情，同情下层人民疾苦，同时以健康、自然的方式体现了追求“美酒、女人和歌”的快乐主义人生哲学。彭斯富有敏锐的幽默感。对苏格兰乡村生活的生动描写使他的诗歌作品具有民族特色和艺术魅力。
除诗歌创作外，彭斯还收集整理大量的苏格兰民间歌谣，编辑出版了6卷本的《苏格兰音乐总汇》和8卷本的《原始的苏格兰歌曲选集》。其中《友谊地久天长》不仅享誉苏格兰，而且闻名世界。
在地球的各个角落，在亲朋的离别或是会议的告别仪式，人们以各种不同语言齐唱《友谊地久天长》(Auld Lang Syne，又名《骊歌》)，朋友们紧紧挽着手，歌唱永不相忘的友谊。它驱走了人们离别的哀愁，使人们满怀激情各奔前程。这首家喻户晓的苏格兰民歌的词作者，即是著名的民族诗人罗伯特·彭斯(Robert Burns，1759－1796)。
彭斯出生在一个贫苦农民家庭，以租地耕种为生。幼时在苏格兰家乡附近上小学。不久校长离去，父亲请老师来家教学。老师认为彭斯兄弟不比年长的同学差。父亲晚上教他们文法及神学。12岁时彭斯二兄弟又轮流去离家四英里的村落上学，14岁在学习英语之余，开始学习法文。
彭斯15岁时成为父亲身边主要的劳动力，驾驭马匹在土墩及洼地上耕作。劳动极为艰辛。虽数次更换土地租耕，因土地贫瘠，收获仍然不佳。劳动之余，彭斯爱读苏格兰诗人申斯通(Shenstone，1714-1793)、蒲柏（Pope，1688-1744）及弗格森（Fergusson 1750-1774）的作品，也浏览苏格兰小说家麦肯齐(MacKenzie，1745-1831)的书。他希望能成为苏格兰艾尔郡(Ayrshire)的诗人，歌唱故乡的山河。
1784年他的父亲去世，全家迁去莫斯吉尔（Mossgiel)，耕作收获并无好转。幸而他的主要用苏格兰方言写的诗集得以出版并迅即获得成功，爱丁堡的出版商又很快为之再版。编辑文学杂志的麦肯齐在评论中称赞这位庄稼汉是位诗歌天才。
于是在爱丁堡，彭斯穿起深色大衣、浅背心、皱边的衬衫，足登鹿皮鞋或长靴，过起出入文学集会及酒馆的双重生活。
彭斯在爱丁堡生活及游历苏格兰一段时期后，仍回乡务农。1788年他考取税务局职员，除了在农田干活外，还要每周在马背上驰骋200英里去上班。执法时，他不放过大鱼，但对贫穷者则手下留情。他为了全力做好税务局的工作，1791年放弃农活迁往邓弗里斯（Dumfries），在那儿度过了他的最后的岁月。
彭斯写了大量的抒情诗，还写讽刺诗及叙事诗。他也喜爱歌曲，有敏锐的音乐耳朵，对节奏有良好的反应。他把最后十年的精力，主要放在为二个丛刊的整理及收集民歌上，使濒临失传的三百多首民歌得以保存下来。
1796年他患了风湿性关节炎及心脏病，于同年7月21日英年早逝。前来送葬的多达二万人。
当年彭斯出生并度过了七年童年的茅屋，位于艾尔郡的阿洛韦镇(Alloway)，现由彭斯纪念碑信托基金机构管理。与茅屋相连接的红瓦顶、前面为长廊及花园的博物馆，为信托机构理事会于1920年所扩建。1994年该理事会重铺稻草屋顶，再建18世纪的菜园及石堤。这就是现在世界著名的彭斯茅舍。
彭斯雕像位于艾尔市中心。从市中心附近坐开往杜恩河老桥(Auld Brig O’Doon)的公共汽车，在终点老桥前一站下车，即见到茅草顶的白色平房，木制的门窗是深棕色的。门上方的黑色纪念板上写着“彭斯茅舍”，接着是“罗伯特·彭斯—艾尔郡诗人”及他的生卒年月。进门后先是谷仓，然后是牛棚及马厩。依稀传来牲畜的叫声，蜡制的耕牛旁还有几只母鸡在啄食谷粒。起居室中以蜡像布置一家人当时融洽的情景。父亲在烛光下读《圣经》，母亲抱着妹妹坐在对面，弟弟坐在一边，彭斯则光着脚站在一旁专心听讲。一个小妹妹躺在摇篮里。厨房里熏黑的炉灶还生着火，彭斯出生的床即在厨房内。布置一如当年。幼年时母亲在这里教孩子们唱苏格兰民歌，姨母则介绍给他们大量有关鬼怪神仙的故事和歌曲。“彭斯茅屋”对崇拜彭斯的人来说是圣地，但也是当年贫苦农家家居生活的写照。
彭斯长期生活在农村，从事繁重的农活。地主的剥削，加上土地的贫瘠，欠收、负债、迁居……使他常常过着没有温饱的生活。但他热爱生活，对劳动人民有深厚的感情。在《两只狗》这首诗里，通过两只分属贫富人家的狗之间的对话，描绘了地主家的骄奢淫逸。贫穷的佃户虽然耕作及劳动辛苦，但同欢共乐聚在一起。而这两家的狗能够融洽相处，成为人类不公平生活的鲜明对照。
诗人也理解农民对牲口的深厚感情。他在《新年早晨老农向老马麦琪致辞》一诗中，回顾了老马一生的辛劳后，写道：“我将在留下的麦地上面，把你的缰绳系好，不用费大力气，你就在那边舒舒畅畅吃个饱。”
与彭斯茅屋相通连的彭斯博物馆，收藏了彭斯珍贵的手稿、他的包括早期版本的作品、有关的画像等，有些收藏品来自美国、加拿大甚至南非。
大展览室介绍他一生的劳动、写作和生活。以图片的形式，配合他的诗句、信件或日记，生动地叙述了他当年的经历。这里还展出了他的怀表、记事本、墨水瓶、鼻烟壶，两把柄上刻有“R.B.”的手枪，以及当税务员测酒用的长棒，也展有1786年版的主要用苏格兰方言写的诗集。笔者自然记得寻找《友谊地久天长》的原稿，它原来出自彭斯于1788年写给友人的一封信中。
第二室展出数幅著名的油画。《羊杂宴》描绘彭斯夫妇款待客人的场景。彭斯喜欢这种热闹场面。另有一组四幅的版画，描绘他的作品《汤姆·奥桑特的故事》(Tam o’Shanter)，彭斯这部根据民间传说写的长诗，讲的是汤姆深夜回家途中遇鬼的故事。他去除了传说中迷信的成分，以喜剧形式讲魔法，寓有深意。同时，它也把儿时听到的传说，与故乡阿罗韦他幼时熟悉的界标、陈旧的教堂、古老的石桥和石冢等联系起来，具有沧桑感和神秘感。
离开这里步行一里，抵达老杜恩河桥，彭斯纪念碑即位于附近的山丘上。这座希腊式建筑由爱丁堡著名建筑师设计，1823年完成，耗资3247镑。登上这座台式纪念碑，可眺望杜恩河及卡里克山(Carrick Hill)的优美景色。纪念碑的基座建有展览室，展出15种外国文字的彭斯著作。在附近花园里，还建有一个雕像室，内有三座《汤姆·奥桑特的故事》中的人物雕像，真人大小，造型风趣。
归途中于老杜恩河老桥公共汽车站，见到一家大百货公司，里面的多种商品以彭斯命名。如果时间合适(回到艾尔的公共汽车每小时一班)，还可看一看介绍彭斯的记录影片。
在他住过的基尔马诺克（Kilmarnock）及邓弗里斯也建有彭斯博物馆、雕像或纪念碑，欧文（lrving）也有他的雕像。甚至远在加拿大和澳大利亚，也有他的纪念碑。位于苏格兰首府爱丁堡的“三作家博物馆”介绍了彭斯、司各特(Walter Scott，1771-1832)及斯蒂文森(Robert L.Stevenson， 1850-1894)的生平，也值得一去。
在彭斯的故乡苏格兰，有数千个彭斯联谊会，苏格兰各地每年都庆祝他的生日。
如此广受故乡人民爱戴的诗人，在世界上也不多见。因为除了长期生活在农村并写出描绘故乡及朴直人民的诗歌外，身受民族压迫的他十分热爱苏格兰，并热情歌颂民主及自由。
在彭斯的青年时代，先后爆发了美国独立战争和法国大革命。他关心世界政治及苏格兰祖国的命运。他写的《华盛顿将军生辰颂》，赞扬美国人民的独立斗争。在法国大革命的影响下，他写了《自由树》和《苏格兰人》两首著名长诗。《自由树》声言有了法兰西这棵自由树，人类将变得平等，世界将获得和平。《苏格兰人》重温历史，以颂扬早年民族英雄华莱士等人的事迹来激励人民：
谁愿为苏格兰国君和法律，
奋力把自由之剑拔出？
生为自由人，死为自由魂，
让他跟我前进！
彭斯是人民的诗人，也是为自由而斗争的战士。这颗明亮的星，永远闪耀在苏格兰的上空，也永远闪耀在爱好和平与友谊的人们心中。
【作品选译】
一朵红红的玫瑰 【英文朗诵：下载地址】
啊，我的爱人象朵红红的玫瑰，
六月里迎风初开，
啊，我的爱人象支甜甜的曲子，
奏得合拍又和谐。
我的好姑娘，多么美丽的人儿！
请看我，多么深挚的爱情！
亲爱的，我永远爱你，
纵使大海干涸水流尽。
纵使大海千涸水流尽，
太阳将岩石烧作灰尘，
亲爱的，我永远爱你，
只要我一息犹存。
珍重吧，我唯一的爱人，
珍重吧，让我们暂时别离，
但我定要回来，
哪怕千里万里！
王佐良译
往昔的时光
老朋友哪能遗忘，
哪能不放在心上？
老朋友哪能遗忘，
还有往昔的时光？
为了往昔的时光，老朋友，
为了往昔的时光，
再干一杯友情的酒，
为了往昔的时光，
你来痛饮一大杯，
我也买酒来相陪。
干一杯友情的酒又何妨？
为了往昔的时光。
我们曾邀游山岗，
到处将野花拜访。
但以后走上疲惫的旅程，
逝去了往昔的时光！
我们曾赤脚瞠过河流，
水声笑语里将时间忘。
如今大海的怒涛把我们隔开，
逝去了往昔的时光！
忠实的老友，伸出你的手，
让我们握手聚一堂，
再来痛饮—杯欢乐酒，
为了往昔的时光！
王佐良译
给我开门，哦！
曲调：轻轻地开门
哦，开门，纵使你对戬无情，
也表一点怜悯，哦。
你虽变了心，我仍忠于糟．
哦，给我开门，哦。
风吹我苍白的双颊，好冷！
但冷不过你对我的心，哦．
冰霜使我心血凝冻，
也没你给我的痛深，哦。
残月沉落白水中，
时间也随我沉落，哦。
假朋友，变心人，永别不再逢！
我决不再采烦渎，哦。
她把门儿大敞开，
见了平地上苍白的尸体，哦，
只喊了一声“爱’就倒在尘埃，
从此再也不起，哦。
王佐良译
走过麦田来
（合唱）啊，珍尼是可怜的人儿，
珍尼哭得悲哀。
她拖着长裙，
走过麦田来。
可怜的人儿，走过麦田来，
走过麦田来，
她拖着长裙
走过麦田来。
如果一个他碰见一个她，
走过麦田来，
如果一个他吻了一个她，
她何必哭起来？
如果一个他碰见一个她
走过山间小道，
如果一个他吻了一个她，
别人哪用知道！
（合唱）啊，珍尼是可怜的人儿，
珍尼哭得悲哀。
她拖着长裙，
走过麦田来。
王佐良译
如果你站在冷风里
呵，如果你站在冷风里，
一人在草地，在草地，
我的小屋会挡住凶恶的风，
保护你，保护你。
如果灾难象风暴袭来，
落在你头上，你头上，
我将用胸脯温暖你，
一切同享，一切同当。
如果我站在可怕的荒野，
天黑又把路迷，把路迷，
就是沙漠也变成天堂，
只要有你，只要有你。
如果我是地球的君王，
宝座我们共有，我们共有，
我的王冠上有一粒最亮的珍珠——
它是我的王后，我的王后。
王佐良译
选自《彭斯诗选》，人民文学出版社(1959)
苏格兰人①
跟华莱士流过血的苏格兰人，
随布鲁斯作过战的苏格兰人，
起来！倒在血泊里也成——
要不就夺取胜利！
时刻已到，决战已近，
前线的军情吃紧，
骄横的爱德华在统兵入侵——
带来锁链，带来奴役！
谁愿卖国求荣？
谁愿爬进懦夫的坟茔？
谁卑鄙到宁做奴隶偷生？——
让他走，让他逃避！
谁愿将苏格兰国王和法律保护，
拔出自由之剑来痛击、猛舞？
谁愿生作自由人，死作自由魂？——
让他来，跟我出击！
凭被压迫者的苦难采起誓，
凭你们受奴役的子孙来起誓，
我们决心流血到死——
但他们必须自由！
打倒骄横的篡位者！
死一个敌人，少一个暴君！
多一次攻击，添一分自由！
动手——要不就断头！
袁可嘉译
①这是彭斯所作爱国诗中最著名的一首，写的是苏格兰
国王罗伯特·布鲁斯在大破英国侵略军的班诺克本一役
(1314)之前向部队所作的号召。首先发表在1794年6月的
《纪事晨报》。
诗中所提的华莱士是一位十三世纪的英格兰民族英雄，
也曾大败英军。但后来为奸人出卖，被执处死。爱德华指
英王爱德华二世。
彭斯一直念念不忘为苏格兰民族独立而斗争的志士，
写此诗时爱国热情尤其澎湃。不仅如此，他还借古讽今，
曾经明白写信告诉朋友说：启发他写这首诗的不止是古代
那场“光荣的争取自由的斗争”，而还有“在时间上却不
是那么遥远的同类性质的斗争”，即法国大革命，当时正
方兴未艾，在苏格兰的彼岸如火如荼地展开。
我的心儿在高原①
我的心儿在高原，我的心不在这儿，
我的心儿在高原，迫遂着鹿儿。
追逐着野鹿，跟踪着獐儿；
我的心儿在高原，不管我上哪儿，
别了啊高原，别了啊北国，
英雄的家乡，可敬的故国，
不管我上哪儿漂荡，我上哪儿遨游，
我永远爱着高原的山丘。
别了啊，高耸的积雪的山岳，
别了啊，山下的溪壑和翠谷，
别了啊，森林和枝檀纵横的树林，
别了啊，急川和洪流的轰鸣，
我的心儿在高原，我的心不在这儿，
我的心儿在高原，追逐着鹿儿，
追逐着野鹿，跟踪着獐儿，
我的心儿在高原，不管我上哪儿。
袁可嘉译
①苏格兰北部地区。
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