Archives/papers-we-love_papers-we-love
1
0
Fork 0
mirror of https://github.com/papers-we-love/papers-we-love.git synced 2024-10-27 20:34:20 +00:00
Code Issues Projects Releases Wiki Activity

Merge pull request #282 from tcfuji/master

Browse Source 
Added a folder for Judea Pearl (2011 Turing Award Winner)
This commit is contained in:
Zeeshan Lakhani 2015-02-12 15:21:43 -05:00
commit c0958c3af6
2 changed files with 31 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
artificial_intelligence/README.md
View File

@ -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 [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 [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.

29
artificial_intelligence/judea_pearl/README.md Normal file
View File

@ -0,0 +1,29 @@
[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.
> -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[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.
> -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[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.
> -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[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.
> -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[1]: http://amturing.acm.org/bib/pearl_2658896.cfm