![]() ![]() When conducting a Monte Carlo simulation, correlation among input variables is an important factor to consider. ppfs is passed as a list of functions which each have one input and one return value. ![]() np.random.multivariate_normal does a lot of the heavy lifting for us, note that in particular we do not need to decompose the correlation matrix. Np.set_printoptions(suppress=True) # suppress scientific notationĪ few note on this function. both percentiles matchį2 = run_gaussian_copula_simulation_and_get_samples( both distributions are independentį1 = run_gaussian_copula_simulation_and_get_samples( Return np.array((U) for i in range(num_dims)]).T # shape($num_samples, $num_dims) # Each row of the returned array will represent one random sample -> access with a # Apply ppf to transform samples into the desired distribution # Transform back into a uniform distribution, i.e. Ran = np.random.multivariate_normal(np.zeros(num_dims), cov_matrix, (num_samples,), check_valid="raise") # Draw random samples from multidimensional normal distribution -> shape($num_samples, $num_dims) Num_samples: int, # number of random samples to draw Ppfs: typing.List, np.ndarray]], # List of $num_dims percentile point functionsĬov_matrix: np.ndarray, # covariance matrix, shape($num_dims, $num_dims) Def run_gaussian_copula_simulation_and_get_samples( ![]()
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