Algorithm

Bayesian probability

Statistics

Bayesian network

Probabilistic logic

Conditional probability

Computer science

Mathematics

Inference

Artificial intelligence

Directed acyclic graph

Machine learning

Bayesian inference

Neurocomputing

As artificial intelligence is being increasingly used for high-stakes applications, it is becoming more and more important that the models used be interpretable. Bayesian networks offer a paradigm for interpretable artificial intelligence that is based on probability theory. They provide a semantics that enables a compact, declarative representation of a joint probability distribution over the variables of a domain by leveraging the conditional independencies among them. The representation consists of a directed acyclic graph that encodes the conditional independencies among the variables and a set of parameters that encodes conditional distributions. This representation has provided a basis for the development of algorithms for probabilistic reasoning (inference) and for learning probability distributions from data. Bayesian networks are used for a wide range of tasks in machine learning, including clustering, supervised classification, multi-dimensional supervised classification, anomaly detection, and temporal modeling. They also provide a basis for estimation of distribution algorithms, a class of evolutionary algorithms for heuristic optimization. We illustrate the use of Bayesian networks for interpretable machine learning and optimization by presenting applications in neuroscience, the industry, and bioinformatics, covering a wide range of machine learning and optimization tasks.

Bayesian networks for interpretable machine learning and optimization