Machine Reasoning using Bayesian Network Ching-Yung Lin, Ph.D. Columbia University and Graphen, Inc. February 28, 2020 Outline Introduction Probability Review Bayesian Network Inference Methods Network Structure Learning Evolution of Intelligence Direction of Evolution recognition perception reasoning sensors strategy representation memory 3 Introduction Suppose the doctor is trying to determine if a patient has inhalational anthrax. She observes the following symptoms: The patient has a cough The patient has difficulty in breathing The patient has a fever 4 Introduction Dealing with uncertainty: You would like to determine how likely the patient is infected with inhalational anthrax given that the patient has a cough, a fever, and difficulty breathing 5 Introduction New evidence: X-ray image shows that the patient has a wide mediastinum. Belief update: your belief that the patient is infected with inhalational anthrax is now much higher now. 6 Introduction In the previous slides, what you observed affected your belief that the patient is infected with anthrax This is called reasoning with uncertainty Wouldn’t it be nice if we had some tools for reasoning with uncertainty? In fact, we do… 7 Bayesian Network Cough Has Anthrax Fever Difficulty Breathing Wide Mediastinum Need a representation and reasoning system that is based on conditional independence Compact yet expressive representation Efficient reasoning procedures Thomas Bayes Bayesian Network is such a representation Named after Thomas Bayes (ca. 1702 –1761) Term coined in 1985 by Judea Pearl (1936 )， 2011 winner of the ACM Turing Award Many applications, e.g., spam filtering, speech recognition, robotics, diagnostic systems and even syndromic surveillance Judea Pearl 8 Outline Introduction Probability Review Bayesian Network Inference methods Network Structure Learning Probabilities We will write P(A = true) to mean the proba