Search Results for "stochastic"
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Showing 187 open source projects for "stochastic"
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SDDP.jl
Stochastic Dual Dynamic Programming in Julia
SDDP.jl is a JuMP extension for solving large convex multistage stochastic programming problems using stochastic dual dynamic programming. -
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DifferentialEquations.jl
Multi-language suite for high-performance solvers of equations
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. The well-optimized DifferentialEquations solvers benchmark as some of the fastest implementations, using classic algorithms and ones from recent research which routinely outperform the “standard” C/Fortran methods, and include algorithms optimized... -
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Sundials.jl
Julia interface to Sundials, including a nonlinear solver
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. -
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OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE)
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. The well-optimized DifferentialEquations solvers benchmark as some of the fastest implementations, using classic algorithms and ones from recent research that routinely outperform the “standard” C/Fortran methods, and include algorithms optimized... -
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models
DSGE.jl is a Julia package developed by the Federal Reserve Bank of New York for estimating and analyzing dynamic stochastic general equilibrium (DSGE) models. It provides tools for Bayesian estimation, filtering, forecasting, and model comparison, supporting both academic research and policy applications. DSGE.jl includes pre-configured models used by central banks and offers extensibility for custom macroeconomic modeling. -
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Turing.jl
Bayesian inference with probabilistic programming
Bayesian inference with probabilistic programming. -
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CellTypist
A tool for semi-automatic cell type classification, harmonization
...Harmonization, match and harmonize cell types defined by independent datasets. integration, integrate cell and cell types with supervision from harmonization. CellTypist recapitulates cell type structure and biology of independent datasets. Regularised linear models with Stochastic Gradient Descent provide a fast and accurate prediction. Scalable and flexible. Python-based implementation is easy to integrate into existing pipelines. A community-driven encyclopedia for cell types. -
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InfiniteOpt.jl
An intuitive modeling interface for infinite-dimensional optimization
...InfiniteOpt.jl provides a general mathematical abstraction to express and solve infinite-dimensional optimization problems (i.e., problems with decision functions). Such problems stem from areas such as space-time programming and stochastic programming. InfiniteOpt is meant to facilitate intuitive model definition, automatic transcription into solvable models, permit a wide range of user-defined extensions/behavior, and more. -
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Catalyst.jl
Chemical reaction network and systems biology interface
...Symbolic ReactionSystems can be used to generate ModelingToolkit-based models, allowing the easy simulation and parameter estimation of mass action ODE models, Chemical Langevin SDE models, stochastic chemical kinetics jump process models, and more. Generated models can be used with solvers throughout the broader SciML ecosystem, including higher-level SciML packages (e.g. for sensitivity analysis, parameter estimation, machine learning applications, etc). -
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NeuroMatch Academy (NMA)
NMA Computational Neuroscience course
NMA Computational Neuroscience course. We have curated a curriculum that spans most areas of computational neuroscience (a hard task in an increasingly big field!). We will expose you to both theoretical modeling and more data-driven analyses. The Neuro Video Series is a series of 12 videos that covers basic neuroscience concepts and neuroscience methods. These videos are completely optional and do not need to be watched in a fixed order so you can pick and choose which videos will help you... -
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DiffOpt.jl
Differentiating convex optimization programs w.r.t. program parameters
...With the help of automatic differentiation, differentiable optimization can have a significant impact on creating end-to-end differentiable systems to model neural networks, stochastic processes, or a game. -
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DeepXDE
A library for scientific machine learning & physics-informed learning
...Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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Diffrax
Numerical differential equation solvers in JAX
Diffrax is a numerical differential equation solving library built for the JAX ecosystem, with a strong focus on composability, differentiability, and high-performance scientific computing. The project provides tools for solving ordinary differential equations, stochastic differential equations, controlled differential equations, and related systems in a way that fits naturally into modern machine learning and differentiable programming workflows. Because it is written to work closely with JAX, it supports just-in-time compilation, automatic differentiation, vectorization, and accelerator-backed execution on hardware such as GPUs and TPUs. ... -
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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. -
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Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. 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... -
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Darts
A python library for easy manipulation and forecasting of time series
darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models... -
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jags-wiener
Wiener functions in JAGS
The JAGS Wiener module is an extension for JAGS, which provides wiener process distribution functions, mainly the Wiener first passage time density. It allows to include stochastic nodes with the first hitting time distribution of a diffusion process. Ubuntu users can also checkout our PPA: https://launchpad.net/~cidlab/+archive/jwm -
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crypto scanner v6.0.0
AI‑powered signals, risk analysis, and automated trading bot
...Live Data: Prices, volume, market cap (CoinMarketCap/CoinGecko), BTC dominance, market sentiment. Smart Signals: Basic (price/volume) & Advanced (RSI, MACD, Bollinger, Stochastic, ADX) with confirmation gate. Risk Rating: Low/Medium/High based on liquidity, volatility, category, technicals. Filters: Category (DeFi, AI, Gaming, NFT, Meme, L2), signal, risk, search. Indicators: RSI, MACD, BB%, Stochastic, ADX, ATR%, EMA, turnover, on‑chain data. Extras: TradingView charts, notes, Excel export.... -
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LINE Solver
Queueing Theory Algorithms
LINE is an open-source software package to analyze queueing models via analytical methods and simulation. The solver is available for Python, MATLAB and Java. LINE features algorithms for the solution of open queueing systems (e.g., M/M/1, M/M/k, M/G/1, ...), open and closed queueing networks, and layered queueing networks. Additional details are available on the project website: http://line-solver.sf.net. -
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Java Modelling Tools is a suite of scientific tools for performance analysis and modelling using queueing theory and colored stochastic Petri nets. Models are solved either with analytical, asymptotic or simulation methods; workload characterization tools are also included in the suite. See the project website for more details: http://jmt.sf.net
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XMDS
Fast integrator of stochastic partial differential equations
XMDS is a code generator that integrates equations. You write them down in human readable form in a XML file, and it goes away and writes and compiles a C++ program that integrates those equations as fast as it can possibly be done in your architecture. -
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Nemo
Individual-based forward-time genetics simulation software
Nemo is an individual-based, forward-time, genetically explicit, and stochastic simulation software designed for the study of the evolution of life history and quantitative traits, and genetic markers under various types of selection, in a spatially explicit, metapopulation framework. -
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Generic data-assimilation toolbox written in java, with native (c and fortran) libraries for high performance computing. Provides tools to couple to your own model and a wide range of algorithms, ranging from parameter calibration to Kalman filters.
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Data Envelopment Analysis using Stata
Unified Data Envelopment Analysis commands for Stata
DEAS provides Data Envelopment Analysis tools for Stata. Version 2.0 is a major update centered on a unified dea command for Stata 16 or later. It consolidates sixteen DEA model families into one interface, keeps backward compatibility with the original inputs = outputs syntax, and adds a fast Mata two-phase revised simplex engine alongside Stata's LinearProgram() IPM engine. The package supports radial technical efficiency (CCR/BCC), SBM, additive model, radial and SBM super-efficiency,... -
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QSMM
An adaptive state model development framework.
QSMM, a recursive acronym for "QSMM State Machine Model", is a framework for learning finite automatons that perform goal-directed interaction with entities which exhibit deterministic or stochastic behavior. The learning process can be carried out in real time together with the interaction process. A basic building block for supporting state models of finite automatons is adaptive probabilistic mapping, which for an argument from its domain returns more often results that maximize or minimize values of one or more objective functions. ...
