Markov chain

Markov chains can be used to model many games of chance.

There have also been other algorithms based on Markov chains.

The Markov chain also can be applied in pattern recognition.

A random walk on a graph is a very special case of a Markov chain.

P values are computed using the first step analysis of Markov chain theory.

The represents the change of the health condition in the underlying Markov chain.

The username is a play on the words "Markov chain".

Markov chain models have been used in advanced baseball analysis since 1960, although their use is still rare.

Frequently, these problems will then be reduced to others involving independent Markov chains.

Markov chains form a common context for applications in probability theory.

Queue theory is based on markov chains and stochastic processes.

However, it does not require a Markov chain structure.

Usually it is not hard to construct a Markov chain with the desired properties.

A finite state machine can be used as a representation of a Markov chain.

It employs a Markov chain procedure in order to determine a new state for a system from a previous one.

In other words, the path followed by the Markov chain of hidden states will be highly random.

Games based entirely on chance can be modeled by an absorbing Markov chain.

This is the same continuous time Markov chain as in a birth-death process.

This page contains examples of Markov chains in action.

His article on Markov chain models has been named one of the four most significant papers in modern actuarial science.

The Markov chain that represents this game contains the following five states:

If both and belong to , the set of natural numbers, then we have models which lead to Markov chains.

Markov chains are used to describe physical processes where a system evolves in constant time.

Markov chains have many applications as statistical models of real-world processes.

The set of all states and transition probabilities completely characterizes a Markov chain.