Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Samplin' Safari is a research tool to visualize and interactively inspect high-dimensional (quasi) Monte Carlo samplers.

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

A Toolkit for Distributional Control of Generative Models

David Mackay's book review and problem solvings and own python codes, mathematica files

My solutions to Yandex Practical Reinforcement Learning course in PyTorch and Tensorflow

Robust estimations from distribution structures: III. Non-asymptotic

[AAAI20] TensorFlow implementation of the Collaborative Sampling in Generative Adversarial Networks

Codebase for "Greedy Shapley Client Selection for Communication-Efficient Federated Learning"

pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets

feyntrop integrates Feynman graphs using tropical sampling

No description provided.

Accompanying source code to my Bachelor's thesis at TUHH

Finding Areas Using the Monte Carlo Method

Real-time brain states tracking system and corticothalamic neural field parameter estimation

Bayesian Non-Parametric Image Segmentation using HDP-MRF

Fun simulations and numerical calculations for the everyday physicist.

CFR repo written with exectution time in mind using C. Repo contains implementations of different CFR variants that can be used with different imperfect information games.

A basic PyTorch implementation of the Collaborative Sampling in Generative Adversarial Networks

Virtual population generation, fitting, and benchmarking.

A simple c++ based ray-tracer rendering-engine with montecarlo sampling integration

This repo contains code to perform Bootstrap Confidence Intervals estimation (a.k.a. Monte Carlo Confidence Interval or Empirical Confidence Interval estimation) for Machine Learing models.

Monte Carlo overview and their applications

This algorithm calculates the zero-point energy of a molecular system by monte-carlo sampling the system's potential energy surface.

A high-performance, production-ready financial modeling framework that combines advanced Monte Carlo simulation techniques with Markov chain models for quantitative finance applications. Built in Python with GPU acceleration support and comprehensive real-time analytics capabilities.

solving a simple 4*4 Gridworld almost similar to openAI gym frozenlake using Monte-Carlo method Reinforcement Learning

Differentiable Probabilistic Models

This program computes the particle pair HBT correlation from Monte-Carlo samples of emitted particles

No description provided.

Codes for statistical test of probabilistic seismic hazard assessments.