In this repository, software applications in simulation and visualization for various applications are presented with interesting examples.
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Updated
Jul 12, 2024 - HTML
In this repository, software applications in simulation and visualization for various applications are presented with interesting examples.
R program for a metropolis-hastings based MCMC sampler using a multivariate-normal proposal distribution.
Inverse prompting LLMs for interpretability
Graph: Representation, Learning, and Inference Methods
Solver of Tetravex puzzle using the Metropolis-Hastings simulated annealing algorithm in C++. demonstrate the effectiveness of the Metropolis-Hastings algorithm in solving combinatorial optimization problems, such as the Tetravex puzzle
Ising model, Glauber dynamics, Metropolis-Hastings algorithms, and renormalization.
Simulations Using Markov Chain Monte Carlo Methods
Python Implementation of Bayesian inference for GMM
Classical predictive models implemented in Python.
The programming part for the second assignment of the course DSC 531 - Statistical Simulations and Data Analysis of the University of Cyprus MSc in Data Science programme
A series of Numerical Simulation examples using various MonteCarlo techniques like Metropolis, Genetic Algos, Simulated Annealing etc.
Metropolis Light Transport (Reading Group)
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Markov Chain Monte Carlo methods.
The repository houses the source code of paper
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
Java and Processing implementations for visualising various MCMC methods.
Notebook for implementing Monte Carlo techniques (Metropolis-Hastings and Augmented Gibbs) to solve a Bayesian Probit regression.
Implementations of optimization techniques, sampling methods and evolutionary algorithms
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