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FunctionSMCMC

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.
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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Clause de non-responsabilité
Les informations et publications ne sont pas destinées à être, et ne constituent pas, des conseils ou recommandations financiers, d'investissement, de trading ou autres fournis ou approuvés par TradingView. Pour en savoir plus, consultez les Conditions d'utilisation.
