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

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

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

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

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

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

Accompanying source code to my Bachelor's thesis at TUHH

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

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

Bayesian Non-Parametric Image Segmentation using HDP-MRF

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

A Toolkit for Distributional Control of Generative Models

Codes for statistical test of probabilistic seismic hazard assessments.

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

My works for EE 511 - Simulation Methods For Stochastic Systems - Spring 2018 - Graduate Coursework at USC - Dr. Osonde A. Osoba

Fun simulations and numerical calculations for the everyday physicist.

Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression

Robust estimations from distribution structures: III. Non-asymptotic

Lattice-Switching Monte Carlo Code for the mW water model