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Plot Markov chain redistributions


h = distplot(___)



distplot(mc,X) creates a heatmap from the data X showing the evolution of a distribution of states in a discrete-time Markov chain mc.


distplot(mc,X,Name,Value) uses additional options specified by one or more Name,Value pair arguments. For example, specify the type of plot or frame rate for animated plots.

h = distplot(___) returns the plot handle. Use h to modify properties of the plot after it is created.


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Create a four-state Markov chain from a randomly generated transition matrix containing eight infeasible transitions.

rng('default'); % For reproducibility
mc = mcmix(4,'Zeros',8);

mc is a dtmc object.

Plot a digraph of the Markov chain.


State 4 is an absorbing state.

Compute the state redistributions at each step for 10 discrete time steps. Assume an initial uniform distribution over the states.

X = redistribute(mc,10)
X =

    0.2500    0.2500    0.2500    0.2500
    0.0869    0.2577    0.3088    0.3467
    0.1073    0.2990    0.1536    0.4402
    0.0533    0.2133    0.1844    0.5489
    0.0641    0.2010    0.1092    0.6257
    0.0379    0.1473    0.1162    0.6985
    0.0404    0.1316    0.0765    0.7515
    0.0266    0.0997    0.0746    0.7991
    0.0259    0.0864    0.0526    0.8351
    0.0183    0.0670    0.0484    0.8663
    0.0168    0.0569    0.0358    0.8905

X is an 11-by-4 matrix. Rows correspond to time steps and columns correspond to states.

Visualize the state redistribution.


After ten transitions, the distribution appears to settle with a majoritty of the probability mass in state 4.

Consider this theoretical, right-stochastic transition matrix of a stochastic process.

Create the Markov chain that is characterized by the transition matrix P.

P = [ 0   0  1/2 1/4 1/4  0   0 ;
      0   0  1/3  0  2/3  0   0 ;
      0   0   0   0   0  1/3 2/3;
      0   0   0   0   0  1/2 1/2;
      0   0   0   0   0  3/4 1/4;
     1/2 1/2  0   0   0   0   0 ;
     1/4 3/4  0   0   0   0   0 ];
mc = dtmc(P);

Compute the state redistributions at each step for 20 discrete time steps.

X = redistribute(mc,20);

Animate the redistibutions in a histogram. Specify a half second frame rate.


Input Arguments

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Discrete-time Markov chain with NumStates states and transition matrix P, specified as a dtmc object.

Evolution of state probabilities, specified as a (1 + numSteps)-by-NumStates nonnegative numeric matrix returned by redistribute. The first row is the initial state distribution. Subsequent rows are the redistributions at each step. distplot normalizes the rows by their respective sums before plotting.

Data Types: double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'Type','graph','FrameRate',3 creates an animated plot of the redistributions using a frame rate of 3 seconds.

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Plot type, specified as the comma-separated pair consisting of 'Type' and a value in this table.


Evolution of the initial distribution. The plot is a (1 + NumSteps)-by-NumStates heatmap. Row i displays the redistribution at step i.


Animated histogram of the redistributions. The vertical axis displays probability mass, the horizontal axis displays states. The 'FrameRate' name-value pair argument controls the animation progress.


Animated graph of the redistributions. distplot colors the nodes by their probability mass at each step. The 'FrameRate' name-value pair argument controls the animation progress.

Example: 'Type','graph'

Data Types: string | char

Length of discrete time steps, in seconds, for animated plots, specified as the comma-separated pair consisting of 'FrameRate' and a positive scalar.

The default is a pause at each time step. The animation proceeds when you press the space bar.

Example: 'FrameRate',3

Data Types: double

Output Arguments

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Plot handle, returned as an object. h is a unique identifier, which you can use to query and modify properties of the plot.

Introduced in R2017b

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