Library "BenfordsLaw" Methods to deal with Benford's law which states that a distribution of first and higher order digits of numerical strings has a characteristic pattern. "Benford's law is an observation about the leading digits of the numbers found in real-world data sets. Intuitively, one might expect that the leading digits of these numbers would be...
Library "MarkovChain" Generic Markov Chain type functions. --- A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. --- reference: Understanding Markov Chains, Examples and Applications. Second Edition. Book by Nicolas...
Library "FunctionProbabilityViterbi" The Viterbi Algorithm calculates the most likely sequence of hidden states *(called Viterbi path)* that results in a sequence of observed events. viterbi(observations, transitions, emissions, initial_distribution) Calculate most probable path in a Markov model. Parameters: observations (int ) : array ....
Library "FunctionBaumWelch" Baum-Welch Algorithm, also known as Forward-Backward Algorithm, uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors. --- ### Function List: > `forward (array pi, matrix a, matrix b, array obs)` > `forward (array pi, matrix a,...
Library "MathProbabilityDistribution" Probability Distribution Functions. name(idx) Indexed names helper function. Parameters: idx : int, position in the range (0, 6). Returns: string, distribution name. usage: .name(1) Notes: (0) => 'StdNormal' (1) => 'Normal' (2) => 'Skew Normal' (3) => 'Student T' (4) => 'Skew Student T' (5)...
Library "FunctionProbabilityDistributionSampling" Methods for probability distribution sampling selection. sample(probabilities) Computes a random selected index from a probability distribution. Parameters: probabilities : float array, probabilities of sample. Returns: int.
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...
Library "Probability" erf(value) Complementary error function Parameters: value : float, value to test. Returns: float ierf_mcgiles(value) Computes the inverse error function using the Mc Giles method, sacrifices accuracy for speed. Parameters: value : float, -1.0 >= _value >= 1.0 range, value to test. Returns: float ierf_double(value) ...