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Probabilistic Machine Learning: Advanced Topics - pml-book "Kevin Murphy's book is a landmark achievement in machine learning It provides an in-depth coverage of a wide range of topics in probabilistic machine learning, from inference methods to generative models and decision making
Probabilistic Machine Learning: An Introduction - pml-book "Kevin Murphy’s book on machine learning is a superbly written, comprehensive treatment of the field, built on a foundation of probability theory It is rigorous yet readily accessible, and is a must-have for anyone interested in gaining a deep understanding of machine learning "
Machine Learning: a Probabilistic Perspective - pml-book by Kevin Patrick Murphy MIT Press, 2012 Key links Buy hardcopy from MIT Press; Buy hardcopy from Amazon com; Winner of De Groot prize in 2013 for best book in Statistical Science Table of contents; Matlab software; All the figures, together with links to the Matlab code to regenerate them Request solution manual (instructors only) Endorsements
What are State Space Models? — State Space Models: A Modern Approach A state space model or SSM is a partially observed Markov model, in which the hidden state, x t, evolves over time according to a Markov process, possibly conditional on external inputs or controls u t, and each hidden state generates some observations y t at each time step
State Space Models: A Modern Approach - pml-book This is an interactive textbook on state space models (SSM) using the JAX Python library Some of the content is covered in other books such as [Sar13] and [Mur23] However, we go into more detail, and focus on how to efficiently implement the various algorithms in a “modern” computing environment, exploiting recent progress in automatic
Bibliography — State Space Models: A Modern Approach - pml-book K P Murphy Probabilistic Machine Learning: Advanced Topics MIT Press, 2023 Rab89 L R Rabiner A tutorial on Hidden Markov Models and selected applications in speech recognition Proc of the IEEE, 77(2):257–286, 1989 RTV12 Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar
Hidden Markov Models — State Space Models: A Modern Approach - pml-book Hidden Markov Models In this section, we discuss the hidden Markov model or HMM, which is a state space model in which the hidden states are discrete, so \ (\hidden_t \in \ {1,\ldots, \nstates\}\) The observations may be discrete, \ (\obs_t \in \ {1,\ldots, \nsymbols\}\), or continuous, \ (\obs_t \in \real^\nstates\), or some combination, as