Search Results for "monte carlo simulation"
Sort By:
Showing 119 open source projects for "monte carlo simulation"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
HOOMD-blue
Molecular dynamics and Monte Carlo soft matter simulation on GPUs
HOOMD-blue is a Python-driven particle simulation engine for molecular dynamics and hard-particle Monte Carlo simulations. It was designed from the ground up for GPU acceleration, with a high-performance C++ and CUDA backend. The software is especially useful for nano-scale, colloidal, polymer, soft matter, and materials simulations. Its Python interface lets users build simulation and analysis workflows using familiar scientific Python tools. -
2
Monte Carlo eXtreme (MCX)
Physically accurate and validated GPU ray-tracer
MCX is a GPU-accelerated, general-purpose, physically-accurate and feature-rich 3-D light transport simulator. It is one of the fastest simulators because it can use tens of thousands of GPU threads to simulate photons in parallel. -
3
QuasiMonteCarlo.jl
Lightweight and easy generation of quasi-Monte Carlo sequences
Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML). This is a lightweight package for generating Quasi-Monte Carlo (QMC) samples using various different methods. -
4
ESPResSo
The ESPResSo package
ESPResSo is a simulation package for molecular dynamics and Monte Carlo simulations of soft matter systems. It is designed for coarse-grained and bead-spring models used in physics, chemistry, and molecular biology. The software can model systems such as polymers, colloids, liquid crystals, ferrofluids, DNA, lipid membranes, and other complex fluids. It includes a broad range of interaction potentials and algorithms for electrostatics, hydrodynamics, and coupled particle-field behavior. ... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
5
Mctx
Monte Carlo tree search in JAX
mctx is a Monte Carlo Tree Search (MCTS) library developed by Google DeepMind for reinforcement learning research. It enables efficient and flexible implementation of MCTS algorithms, including those used in AlphaZero and MuZero. -
6
EMC: Enhanced Monte Carlo; A multi-purpose modular and easy extendable solution to molecular and mesoscale simulations
-
7
DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent of coding a (log) posterior density in Julia. ... -
8
Prophylactic-replacement-of-voice-prosthesis---Suppl-Monte-Carlo-simulations
Monte-Carlo simulations for: Prediction of prophylactic replacement of voice prosthesis in laryngectomized patients – A retrospective cohort study
-
9
TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
...TFP is open source and available on GitHub. Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction. Variational inference and Markov chain Monte Carlo. A wide selection of probability distributions and bijectors. Optimizers such as Nelder-Mead, BFGS, and SGLD. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
10
Gen.jl
A general-purpose probabilistic programming system
...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. ... -
11
Integrals.jl
A common interface for quadrature and numerical integration
Integrals.jl is an instantiation of the SciML common IntegralProblem interface for the common numerical integration packages of Julia, including both those based upon quadrature as well as Monte-Carlo approaches. By using Integrals.jl, you get a single predictable interface where many of the arguments are standardized throughout the various integrator libraries. This can be useful for benchmarking or for library implementations since libraries that internally use a quadrature can easily accept an integration method as an argument. -
12
Meridian
Meridian is an MMM framework
...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. -
13
Kimi k1.5
Scaling Reinforcement Learning with LLMs
...By using techniques like partial rollouts to improve training efficiency and applying sophisticated policy optimization methods, the developers demonstrate that strong ability can emerge without relying on complex solutions like Monte Carlo tree search or value functions. Kimi-k1.5 is trained jointly on text and vision data, giving it true multimodal reasoning capabilities where it can interpret and generate content across modalities in a unified way. -
14
Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used... -
15
RMCProfile
Big box modeling for total scattering via the Reverse Monte Carlo
-
16
Uncertainty Baselines
High-quality implementations of standard and SOTA methods
...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. -
17
Slot math toolkit
Python toolkit for slot machine RTP verification and variance analysis
Slot Math Toolkit is an open-source Python library for slot machine math: RTP verification via Monte Carlo simulation, Bonus Buy ROI analysis, variance scoring (1-5 scale), and single-session simulation. Sample data for Sugar Rush series (Original, 1000, Super Scatter), Sweet Bonanza, Gates of Olympus, Mega Joker, Ugga Bugga. For the full Sugar Rush Super Scatter methodology including paytable breakdowns and Super Scatter multiplier ranges (x100/x500/x5000/x50000), see the detailed mechanics breakdown at https://sugarrush-super-scatter.com/ Core features: - RTP Calculator with 95% confidence intervals - Bonus Buy ROI Analyzer (x100 standard, x500 super) - Variance Scorer via coefficient of variation - Session Simulator with bankroll, bet size, stop-loss Install: pip install slot-math-toolkit CLI: slot-math rtp --slot sugar_rush_super_scatter --spins 100000 41 tests, 94% coverage. ... -
18
ntuacarlo
A open-source implementation library of optimization algorithms
ntuacarlo: library of open-source implementation of optimization algorithms for Matlab/GNU Octave The library implements matlab functions for the following optimization algorithms: Simulated Annealing Particle Swarm Optimization Monte Carlo Exhaustive search The signature of the functions follow the same as the ga() function of Matlab. All the functions support lower and upper bounds, linear and non-linear constraints, and integer variables.Octave -
19
Statistics101 - Resampling Statistics
Use simulation to perform statistical analyses.
...Anyone who wants to learn statistics will find that the resampling approach helps in understanding statistical concepts from the simplest to the most difficult. In addition, professionals who want to use resampling, bootstrapping, or Monte Carlo simulations will find Statistics101 helpful. More information at https://statistics101.sourceforge.io/ -
20
JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.
-
21
MCPower
MCPower — simple Monte Carlo power analysis for complex models
MCPower-GUI is a desktop application that provides a graphical interface for the MCPower Monte Carlo power analysis library. It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). ... -
22
GeoDMA
Geographic feature extraction and data mining
GeoDMA is a plugin for TerraView software, used for geographical data mining. With a single image, the user can perform segmentation, attributes extraction, normalization and classification. -
23
...The code includes built-in fitting procedures with a wide variety of constraints; stochastic SR energy loss; the tracking of synchrotron radiation (SR) Poynting vector; space charge models; various Monte Carlo procedures, etc. Contact: francoisgmeot@gmail.com Documentation (History of accelerators that zgoubi deals with, theory, tutorials): https://link.springer.com/book/10.1007/978-3-031-59979-8 https://link.springer.com/book/10.1007/978-3-031-16715-7, Chap. 14.
-
24
UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
25
Basic Blind Chess
Blind Chess using Taiwan rules
...This application is using Taiwan rule. AI is using basic minimax tree structure and some rules. Advanced algorithm such as Upper Confidence bounds applied to Trees and Monte Carlo Tree Search are NOT used. This app is single player vs computer only. For English user, Christakis Giorgios made Software Requirements Specification(pdf file), it will help anyone that downloads this application to understand basic blind chess and use it. You can download this pdf in Files->Specification folder.
