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Markov Chain Models

Discrete state-space processes characterized by transition matrices

For an overview of the Markov chain analysis tools, see Markov Chain Modeling.

Using Objects

dtmcCreate discrete-time Markov chain


dtmcCreate discrete-time Markov chain
mcmixCreate random Markov chain with specified mixing structure
asymptoticsDetermine Markov chain asymptotics
isergodicCheck Markov chain for ergodicity
isreducibleCheck Markov chain for reducibility
classifyClassify Markov chain states
lazyAdjust Markov chain state inertia
subchainExtract Markov subchain
redistributeCompute Markov chain redistributions
simulateSimulate Markov chain state walks
distplotPlot Markov chain redistributions
eigplotPlot Markov chain eigenvalues
graphplotPlot Markov chain directed graph
simplotPlot Markov chain simulations


Discrete-Time Markov Chains

Markov chains are discrete-state Markov processes described by a right-stochastic transition matrix and represented by a directed graph.

Markov Chain Modeling

The dtmc class provides basic tools for modeling and analysis of discrete-time Markov chains. The class supports chains with a finite number of states that evolve in discrete time with a time-homogeneous transition structure.

Create and Modify Markov Chain Model Objects

Create a Markov chain model object from a state transition matrix of probabilities or observed counts, or a random Markov chain with a specified structure.

Visualize Markov Chain Structure and Evolution

Visualize the structure and evolution of a Markov chain model using dtmc plotting functions.

Determine Asymptotic Behavior of Markov Chain

Compute the stationary distribution of a Markov chain, estimate its mixing time, and determine whether the chain is ergodic and reducible.

Compare Markov Chain Mixing Times

Compare the estimated mixing times of several Markov chains having differing structures.

Identify Classes in Markov Chain

Programmatically and visually identify classes in a Markov chain.

Simulate Random Walks Through Markov Chain

Generate and visualize random walks through a Markov chain.

Compute State Distribution of Markov Chain at Each Time Step

Compute and visualize state redistributions, which is the evolution of the deterministic state distributions over time from an initial distribution.

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