Some methods to sampling data points from a given distribution.
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Updated
Jul 16, 2018 - Python
Some methods to sampling data points from a given distribution.
Adaptive Rejection Sampling for Python
Monte Carlo methods with TensorFlow
Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.
Code library for the DRMD framework from 'DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift'.
Implementation for bayesian network
Recursos sobre manejo de la incertidumbre y probabilidad por un agente inteligente, mĂłdulo de Modelos de Inteligencia Artificial
python implementation for rejection sampling and importance sampling
A Python implementation of Bayesian Networks from scratch, featuring exact inference (Variable Elimination) and approximate inference algorithms (Rejection Sampling, Gibbs Sampling, and Likelihood Weighting).
Official PyTorch implementation of "LSRS: Latent Scale Rejection Sampling for Visual Autoregressive Modeling". An efficient test-time scaling strategy to enhance VAR image generation quality with minimal overhead.
Application of rejection sampling and markov chain monte carlo (MCMC) algorithms to approximate bayesian computation (ABC). The project includes application of ABC to model the pharmacokinetics of theophylline.
Implementation of Prior Sampling, Rejection Sampling, Likelihood Weighting, and Gibbs Sampling for Bayesian Network Stochastic Inference.
Poker test for independence, Inversion method, Method of approximations, Rejection method, Quadratic congruent random number generator, Freedman–Diaconis rule, Fixation Index, Extended Haplotype Homozygosity, Wright-Fisher model
Implementation of Prior, Rejection, Likelihood and Gibbs Sampling
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