Probabilistic modeling and inference -- Dynamical systems and Markov processes -- Likelihoods and latent variables -- Bayesian inference -- Computational inference -- Regression models -- Mixture models -- Hidden Markov models -- State-space models -- Continuous time models.
Summary:
"This accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling and analysis techniques. Aimed at students and researchers in the sciences, the text is self-contained and pedagogical, including practical examples and end of chapter problems"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.