Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Added a folder for Judea Pearl (2011 Turing Award Winner) #282

Merged
merged 2 commits into from Feb 12, 2015
Merged

Added a folder for Judea Pearl (2011 Turing Award Winner) #282

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e765cd7
Added Judea Pearl folder
Feb 12, 2015
f80f74d
Improve README markdown
tcfuji Feb 12, 2015
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
2 changes: 2 additions & 0 deletions artificial_intelligence/README.md
View file
Open in desktop
Expand Up @@ -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.
29 changes: 29 additions & 0 deletions artificial_intelligence/judea_pearl/README.md
View file
Open in desktop
@@ -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