From e765cd7de8cde5615ad3495792b2bf996c11df0e Mon Sep 17 00:00:00 2001 From: Ted Fujimoto Date: Thu, 12 Feb 2015 02:39:49 -0500 Subject: [PATCH 1/2] Added Judea Pearl folder --- artificial_intelligence/README.md | 2 ++ artificial_intelligence/judea_pearl/README.md | 27 +++++++++++++++++++ 2 files changed, 29 insertions(+) create mode 100644 artificial_intelligence/judea_pearl/README.md diff --git a/artificial_intelligence/README.md b/artificial_intelligence/README.md index d33c59a..a4cf9dc 100644 --- a/artificial_intelligence/README.md +++ b/artificial_intelligence/README.md @@ -3,3 +3,5 @@ [Analysis of Three Bayesian Network Inference Algorithms:Variable Elimination, Likelihood Weighting, and Gibbs Sampling](https://github.com/papers-we-love/papers-we-love/blob/master/artificial_intelligence/3-bayesian-network-inference-algorithm.pdf) by Rose F. Liu, Rusmin Soetjipto [Computing Machinery and Intelligence](http://www.csee.umbc.edu/courses/471/papers/turing.pdf) by A.M. Turing + +[Judea Pearl](http://bayes.cs.ucla.edu/jp_home.html) folder - Papers by Judea Pearl, 2011 winner of the ACM Turing Award. diff --git a/artificial_intelligence/judea_pearl/README.md b/artificial_intelligence/judea_pearl/README.md new file mode 100644 index 0000000..c168de6 --- /dev/null +++ b/artificial_intelligence/judea_pearl/README.md @@ -0,0 +1,27 @@ +[Reverend Bayes on inference engines: A distributed hierarchical approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) - +> The paper that began the probabilistic revolution in AI +> by showing how several desirable properties of reasoning systems +> can be obtained through sound probabilistic inference. +> It introduced tree-structured networks as concise representations of +> complex probability models, identified conditional independence +> relationships as the key organizing principle for uncertain knowledge, +> and described an efficient, distributed, exact inference algorithm. +> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) + +[A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma. +> Introduces minimal-model semantics as a basis for causal discovery, +> and shows that causal directionality can be inferred from patterns +> of correlations without resorting to temporal information. +> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) + +[Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked. +> Introduces the theory of causal diagrams and its associated do-calculus; +> the first (and still the only) mathematical method to enable a +> systematic removal of confounding bias in observations. +> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) + +[The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) - +> Describes a computational model that explains how humans generate, +> evaluate and distinguish counterfactual statements so swiftly and +> consistently. +> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) From f80f74dfea0d6de67aecf094ddccda63a1a60652 Mon Sep 17 00:00:00 2001 From: Ted Fujimoto Date: Thu, 12 Feb 2015 02:47:43 -0500 Subject: [PATCH 2/2] Improve README markdown --- artificial_intelligence/judea_pearl/README.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/artificial_intelligence/judea_pearl/README.md b/artificial_intelligence/judea_pearl/README.md index c168de6..533e8fa 100644 --- a/artificial_intelligence/judea_pearl/README.md +++ b/artificial_intelligence/judea_pearl/README.md @@ -1,4 +1,4 @@ -[Reverend Bayes on inference engines: A distributed hierarchical approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) - +[Reverend Bayes on inference engines: A distributed hierarchical approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) - > The paper that began the probabilistic revolution in AI > by showing how several desirable properties of reasoning systems > can be obtained through sound probabilistic inference. @@ -6,22 +6,24 @@ > complex probability models, identified conditional independence > relationships as the key organizing principle for uncertain knowledge, > and described an efficient, distributed, exact inference algorithm. -> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) +> -- [ACM Turing Award Short Annotated Bibliography][1] [A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma. > Introduces minimal-model semantics as a basis for causal discovery, > and shows that causal directionality can be inferred from patterns > of correlations without resorting to temporal information. -> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) +> -- [ACM Turing Award Short Annotated Bibliography][1] [Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked. > Introduces the theory of causal diagrams and its associated do-calculus; > the first (and still the only) mathematical method to enable a > systematic removal of confounding bias in observations. -> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) +> -- [ACM Turing Award Short Annotated Bibliography][1] [The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) - > Describes a computational model that explains how humans generate, > evaluate and distinguish counterfactual statements so swiftly and > consistently. -> [ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm) +> -- [ACM Turing Award Short Annotated Bibliography][1] + +[1]: http://amturing.acm.org/bib/pearl_2658896.cfm