September 2018
Beginner to intermediate
178 pages
4h 57m
English
Content preview from Hands-On Markov Models with Python
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Markov processes
A stochastic process is called a Markov process if the state of the random variable at the next instance of time depends only on the outcome of the random variable at the current time. In simplistic mathematical terms, for a stochastic process, S = {R1, R2, . . ., Rn} = {R}t=1, . . ., n, to be a Markov process, it must satisfy the following condition:
According to the previous condition, the probability distribution for any variable at any given instance in a Markov process is a conditional distribution, which is conditioned only on the random variable at the last time instance. This property of a system, such that the future ...
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