importance-sampling

The Noise Contrastive Estimation for softmax output written in Pytorch

Ray tracing glTF scene with Vulkan

Awesome Domain Adaptation Python Toolbox

Clustering with variational Bayes and population Monte Carlo

Python Example of Monte Carlo Estimation & Importance Sampling

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

📷 webGL2 path tracing experiment.

Fast and memory-efficient clustering + coreset construction, including fast distance kernels for Bregman and f-divergences.

Implementation of the paper: Adaptive BRDF-Oriented Multiple Importance Sampling of Many Lights

An elegant adaptive importance sampling algorithms for simulations of multi-modal distributions (NeurIPS'20)

Massively Parallel Simulator of Optical Coherence Tomography (OCT-MPS)

vk_shaded_gltfscene

Pareto smoothed importance sampling

Expectation Particle Belief Propagation code

Experimental code: adaptive importance sampling for bayesian networks. -- I think I may have broken this code.

Some methods to sampling data points from a given distribution.

Markov Chain Monte Carlo (MCMC) and importance sampling in the context of Bayesian linear regression

This repository includes Matlab codes/routines that were used in our manuscript entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" that can be found in this preprint: https://arxiv.org/abs/1911.06286

Code accompanying the notMIWAE paper

Enumerating Search and Weighing of Trapping sets in LDPC codes, Importance Sampling

Collection of reinforcement learning algorithms implementations with TensorFlow2

Importance Weighted Autoencoders in TensorFlow 2, reproducing results from the IWAE paper with 1 or 2 stochastic layers

Numerical Simulation Laboratory at Unimi in 2020-2021 (D.E. Galli). Advanced Monte Carlo methods: Markov chains, Metropolis algorithm. Numerical simulations in statistical mechanics. Stochastic calculus and stochastic differential equation. Computational intelligence, stochastic optimization. Parallel computing and parallel programming. Machine learning and deep neural networks

Sampling from manifold-restricted Gaussians.

R package with quasi-Monte Carlo methods to estimate mixed models commonly used for random effect structures from pedigrees.

Importance sampling with transformed weights