论文信息 - Learning with CASTLE

Learning with CASTLE

We will describe here the learning algorithms we have implemented in CASTLE, (Causal Structures From Inductive Learning), to learn about causal structures from examples. A brief introduction to the software ifself and a description of what we intend to develop and implement in CASTLE are also given. Finally, the use of CASTLE is illustrated on a simple example.

[1] Alfred V. Aho,et al. Data Structures and Algorithms , 1983 .

[2] Judea Pearl,et al. Chapter 2 – BAYESIAN INFERENCE , 1988 .

[3] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[4] Czesław Rajski. On the normed information rate of discrete random variables , 1962 .

[5] B. Ripley. Statistical inference for spatial processes , 1990 .

[6] Judea Pearl,et al. Equivalence and Synthesis of Causal Models , 1990, UAI.

[7] L. N. Kanal,et al. Uncertainty in Artificial Intelligence 5 , 1990 .

[8] B. Ripley,et al. Using spatial models as priors in astronomical image analysis , 1989 .

[9] David J. Spiegelhalter,et al. Probabilistic Reasoning in Predictive Expert Systems , 1985, UAI.

[10] David J. Spiegelhalter,et al. Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.