Search Results for "monte carlo"

...Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. ...

...The framework provides a robust foundation for constructing in-house MMM pipelines capable of handling both national and geo-level data, with built-in support for calibration using experimental data or prior knowledge. Meridian uses the No-U-Turn Sampler (NUTS) for Markov Chain Monte Carlo (MCMC) sampling to produce statistically rigorous results, and it includes GPU acceleration to significantly reduce computation time.

...The library spans canonical modalities and tasks, from image classification and NLP to tabular problems, with baselines that cover both deterministic and probabilistic approaches. Techniques include deep ensembles, Monte Carlo dropout, temperature scaling, stochastic variational inference, heteroscedastic heads, and out-of-distribution detection workflows. Each baseline emphasizes reproducibility: fixed seeds, standard splits, and strong metrics such as calibration error, AUROC for OOD, and accuracy under shift.

JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.

Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so users can experiment with agents learning in simulated tasks. For each algorithm category, the repository pairs conceptual descriptions with runnable code and often illustrated exercises that help solidify understanding by bridging theory with practice. ...

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.

Programmer's library for random numbers. Also random number generator testing code. Intended for simulation, games and "Monte-Carlo" algorithms.

...It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). RStan integrates with Stan’s automatic differentiation library, provides diagnostics, model comparison, posterior predictive checks, etc. It is used in research, applied statistics, and modelling workflows where flexibility and rigor in Bayesian methods are required.

That project aims at studying the impact of IT systems interactions on traveller demand and airline revenues. Passenger demand is generated (Monte Carlo) and injected into simulated CRS and airline IT systems. Differential analysis is then performed on various changes compared to a bottom line scenario.

Free C++ toolkit to facilitate Monte-Carlo simulation. This is a library covered under the LGPL. "MCS-libre" stands for "Monte Carlo Simulation - libre". Documentation and examples are provided.

SPSens is a complete software package written in C that estimates parameter sensitivities for stochastic models of chemical and biochemical reaction networks using Monte Carlo (MC) stochastic simulations. It is possible to estimate sensitivities with respect to system parameters using the following algorithms: finite difference methods (crude monte carlo, common reaction path, coupled finite differences); likelihood ratio methods; and regularized pathwise derivatives. Additionally the package includes basic stochastic simulation algorithms. ...

Tina's Random Number Generator Library (TRNG) is a state of the art C++ pseudo-random number generator library for sequential and parallel Monte Carlo simulations.

The Tomographic Image Reconstruction Interface of the Universite de Sherbrooke (TIRIUS) is a Qt-based user-interface software for reconstructing 3D images from data generated by real apparatus or generated by the GATE Monte Carlo simulator.

TAROT is a easy-to-use framework for Monte Carlo simulations in python. Calculations between different kinds of randomly distributed numbers are made as easy as basic arithmetics. Tarot provides an interactive graphical interface for interpretation.

The Molecular Modeling Templates, MMT is a C++ class library for molecular simulation applications. MMT serves as a code basis that can be easily extended and modified to perform Monte Carlo and molecular dynamics simulations.