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2016 PGM PGM 2016

Conditional Probability Estimation

Abstract

This paper studies in particular an aspect of the estimation of conditional probability distributions by maximum likelihood that seems to have been overlooked in the literature on Bayesian networks: The information conveyed by the conditioning event should be included in the likelihood function as well.

🚀 Conference Pioneer — PGM 2016
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🐣 Hot Topic Early Bird — maximum likelihood

Authors

Marco E. G. V. Cattaneo

Topics

Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling

Keywords

maximum likelihood probability distribution conditional probability likelihood function bayesian network
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