Markov Model (Chain, Discrete Time, Continuous Time, Hidden)
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Wikipedia says, "A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states." What does that even mean? Introducing Hidden Markov Models (HMM) | Linear Digressions
Predecessor to Boltzmann machines (BM) and Hopfield network (HN). They can be understood as follows: from this node where I am now, what are the odds of me going to any of my neighbouring nodes? They are memoryless (i.e. Markov Property) which means that every state you end up in depends completely on the previous state. While not really a neural network, they do resemble neural networks and form the theoretical basis for BMs and HNs. MC aren’t always considered neural networks, as goes for BMs, RBMs and HNs. Markov chains aren’t always fully connected either. Hayes, Brian. “First links in the Markov chain.” American Scientist 101.2 (2013): 252.
Contents
Markov Chain (MC)
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Discrete Time Markov Chain (DTMC)
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Continuous Time Markov Chain (CTMC)
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Hidden Markov Model (HMM)
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- Part-of-Speech (POS) Tagging
- Phylogenetic Hidden Markov Models | Adam Siepel and David Haussler
- Hidden Markov Models in Bioinformatics | V. De Fonzo, F. Aluffi-Pentini, and V. Parisi
Viterbi Algorithm
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