markov chain in matlab

Here is an example of implementing a simple Markov chain using MATLAB:

main.m
% Define transition matrix
trans_mat = [0.7 0.3; 0.2 0.8];

% Define initial state probabilities
init_prob = [0.5; 0.5];

% Generate state sequence
num_states = 100;
state_seq = zeros(num_states, 1);
state_seq(1) = randi(2); % initial state
for i = 2:num_states
    state_seq(i) = randsrc(1, 1, [1 2; trans_mat(state_seq(i-1), :)]);
end
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In this example, trans_mat is the transition matrix that defines the probability of transitioning from one state to another. init_prob is a column vector that defines the initial probability of each state. state_seq is a column vector that stores the state sequence generated by the Markov chain.

The randi function is used to randomly select the initial state from either state 1 or state 2. The randsrc function is then used to generate the subsequent states in the sequence based on the transition probabilities defined in trans_mat and the current state.

Note that the randsrc function is not part of the base MATLAB package, so you may need to install the Communications Toolbox to use it. Alternatively, you can use the rand function to generate a uniform random number between 0 and 1 to determine the next state in the sequence based on the transition probabilities.