diff --git a/artificial_intelligence/judea_pearl/README.md b/artificial_intelligence/judea_pearl/README.md index 435226d..5498c12 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. @@ -7,17 +7,17 @@ > relationships as the key organizing principle for uncertain knowledge, > and described an efficient, distributed, exact inference algorithm. -[A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma. +📜[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. -[Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked. +📜[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. -[The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) - +📜[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.