Library "FunctionSMCMC" Methods to implement Markov Chain Monte Carlo Simulation (MCMC)
markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm Parameters: weights: float array, weights of the Markov Chain. actions: float array, actions of the Markov Chain. target_path: float array, target path array. position: int, index of the path. last_value: float, base value to increment. Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step. Parameters: weights: float array, weights of the Markov Chain. actions: float array, actions of the Markov Chain. start_value: float, base value to start simulation. n_iterations: integer, number of iterations to run. Returns: float array with path.
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added option to pass weights(probability)/actions as inputs.