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I am analyzing a discrete-time Markov chain that can grow exponentially but also suffers from frequent, severe drops. I want to find the exact probability that it reaches a certain threshold within a
Probability of a Markov chain $X_n \sim U (1, 2 X_ {n-1})$ reaching ...
I would like to know what books people currently like in Markov Chains (with syllabus comprising discrete MC, stationary distributions, etc.), that contain many good exercises. Some such book on
reference request - What are some modern books on Markov Chains with ...
When I started doing machine learning in the 1990s, many practitioners and researchers used Hidden Markov Models which are measure theoretically isomorphic to Markov models, but have 2 matrices. One is an observation matrix and the other is a transition matrix which models probabilities between hidden states.
A Markov chain is a discrete-valued Markov process. Discrete-valued means that the state space of possible values of the Markov chain is finite or countable. A Markov process is basically a stochastic process in which the past history of the process is irrelevant if you know the current system state. In other words, all information about the past and present that would be useful in saying ...
A Gauss-Markov process is a random process that is both a Gaussian process and a Markov process. What is the difference between them? Are there Gauss-Markov processes that are not Gaussian random walks?