Bayesian inference offers a principled mechanism for updating beliefs by combining prior distributions with observed data via Bayes’s theorem. Central to this approach is the specification of a ...
IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...
Bayesian networks have become popular tools for enterprise data scientists working with prediction, as the rise of cheap and abundant cloud computing has made way for adaptable infrastructure. In the ...
Active inference, and in particular its recent application to partially observable Markov decision processes, offers a unified mathematical framework for modelling perception, learning, and decision ...
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