write the full code for parameter estimation of several parametrs using mcmc in matlab, consider input parameters have uniform prior and take values for example in matlab in matlab in javascript

main.m
% Define the log likelihood function
function loglikelihood = log_likelihood(parameters, data)
    % Data likelihood - Implement based on your problem
end

% Define the log prior function
function logprior = log_prior(parameters)
    % Uniform prior distribution - Implement based on your problem
end

% Define the log posterior function - log(posterior) = log(likelihood) + log(prior)
function logposterior = log_posterior(parameters, data)
    logposterior = log_likelihood(parameters, data) + log_prior(parameters);
end

% Initialize MCMC parameters
nIterations = 10000;
nParams = 5; % Number of parameters
proposal_variance = 0.1; % Variance for the proposal distribution

% Initialize parameter values
params_current = some_initial_values; % Set initial parameter values

% Initialize chain to store samples
chain = zeros(nIterations, nParams);

% Implement the Metropolis-Hastings MCMC algorithm
for iter = 1:nIterations
    % Propose new parameter values
    params_proposed = params_current + randn(1, nParams) * proposal_variance;
    
    % Compute acceptance probability
    accept_prob = min(1, exp(log_posterior(params_proposed, data) - log_posterior(params_current, data)));
    
    % Generate a random number to decide acceptance
    if rand < accept_prob
        % Accept proposal
        params_current = params_proposed;
    end
    
    % Store the current state in the chain
    chain(iter, :) = params_current;
end

% Posterior samples are stored in the 'chain' variable
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index.tsx
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