Showing 12 open source projects for "markov"

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  • 1
    POMDPs

    POMDPs

    Interface for defining, solving, simulating Markov decision processes

    A Julia interface for defining, solving and simulating partially observable Markov decision processes and their fully observable counterparts. The POMDPs.jl package contains only the interface used for expressing and solving Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). The POMDPTools package acts as a "standard library" for the POMDPs.jl interface, providing implementations of commonly-used components such as policies, belief updaters...
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  • 2
    Clustering.jl

    Clustering.jl

    A Julia package for data clustering

    Methods for data clustering and evaluation of clustering quality.
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  • 3
    NeuralOperators.jl

    NeuralOperators.jl

    DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia

    ... spaces. The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. It performs Fourier transformation across infinite-dimensional function spaces and learns better than neural operators. Markov neural operator learns a neural operator with Fourier operators.
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  • 4
    Sundials.jl

    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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  • 5
    Bayesian Statistics

    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...
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  • 6
    DifferentialEquations.jl

    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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  • 7
    OrdinaryDiffEq.jl

    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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  • 8
    Gen.jl

    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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  • 9
    MonteCarlo.jl

    MonteCarlo.jl

    Classical and quantum Monte Carlo simulations in Julia

    MonteCarlo.jl is a package implementing classical and quantum Monte Carlo simulations primarily for solid state physics. Currently the focus is on finding a overall design for the package and verifying that determinant Quantum Monte Carlo (DQMC) is working correctly. As such the package may still go through significant changes and the documentation may be outdated and incomplete. Note that classical Monte Carlo is also not a focus at this point. It is probably usable, but a lot of the...
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  • 10
    MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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  • 11
    Bridge.jl

    Bridge.jl

    A statistical toolbox for diffusion processes

    Statistics and stochastic calculus for Markov processes in continuous time, include univariate and multivariate stochastic processes such as stochastic differential equations or diffusions (SDE's) or Levy processes.
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  • 12
    HMMBase.jl

    HMMBase.jl

    Hidden Markov Models for Julia

    HMMBase is not maintained anymore. It will keep being available as a Julia package but we encourage existing and new users to migrate to HiddenMarkovModels.jl which offers a similar interface. For more information see HiddenMarkovModels.jl: when did HMMs get so fast?. HMMBase provides a lightweight and efficient abstraction for hidden Markov models in Julia. Most HMMs libraries only support discrete (e.g. categorical) or Normal distributions. In contrast HMMBase builds upon Distributions.jl...
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