Showing 12 open source projects for "markov"

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  • Achieve perfect load balancing with a flexible Open Source Load Balancer Icon
    Achieve perfect load balancing with a flexible Open Source Load Balancer

    Take advantage of Open Source Load Balancer to elevate your business security and IT infrastructure with a custom ADC Solution.

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  • The best tech for studios, swimming, and sport businesses Icon
    The best tech for studios, swimming, and sport businesses

    SportsEngine Motion keeps you in control of your class-based business, swim club, or swim school—from an all-inclusive dashboard and mobile app.

    Get a clear view of all your business finances on one easy-to-read dashboard and effortlessly charge, track, and collect payments for class registrations, competitions, events—or anything else. Monitor finances and payments on your SportsEngine Motion website and mobile app so you’re always in the know. Customize and run detailed and exportable financial reports to view nearly anything, including the status of all accounts, incoming money, past-due accounts, and more. Seamlessly accept and automatically process credit card payments and access reports to see the status of every payment.
  • 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
    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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  • SPC for Excel Software simplifies statistical charting and analysis. Icon
    SPC for Excel Software simplifies statistical charting and analysis.

    For anyone interested in statistical data analysis and process improvement

    SPC for Excel Software simplifies statistical charting and analysis. SPC for Excel helps you identify problem areas, gain insights into your data, spot trends, solve problems and improve processes all in the familiar environment of Excel. Whether you are a seasoned Black Belt or a novice, interested in Lean Six Sigma, Quality or process improvements, this software is for you. Get all the techniques you need for your charting and analysis. Technical support is free and there are no yearly fees. Discounts for multiple users. Site and enterprise licenses are available.
  • 5
    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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  • 6
    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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  • 7
    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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  • 8
    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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  • 9

    Kinetic Model Builder

    GUI for building, simulating and optimizing kinetic models.

    A 3D graphical interface for building, simulating and optimizing Markov kinetic models in response to user defined time dependent stimuli. Models can be represented as either states and connecting transitions or interacting allosteric elements (see http://dx.doi.org/10.1085/jgp.201411183). Model parameters can be optimized by fitting model responses to user data. Optional user defined constraint equations for model parameters are parsed with EigenLab (https://github.com/marcel-goldschen-ohm...
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  • Get your medical device MDR compliant faster. Icon
    Streamline every stage of the development lifecycle with a flexible solution built for SxMD. Efficiently manage design controls from requirements to compliance with end-to-end traceability to mitigate risk.
  • 10
    JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small...
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  • 11
    With miao3d you can train a specific Gaussian Markov Random Field (GMRF) that then can be used to estimate a depthmap ("3D"), given an image ("2D"). A GUI allows inspection of the image + depthmap.
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  • 12

    Petri Nets Editor

    Simple petri net editor

    Simple editor and analyzer of Petri nets. It allows you to build reachability tree with/without instant transitions and Markov chains. It is also possible to set transition distributions and set all the parameters of the current distribution. This project was made as a course project at NTUU "KPI" during attending classes "Computer modelling". Feel free to make any changes to the current version or ask me if you have any question.
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