Showing 7 open source projects for "markov"

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  • 1
    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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    Downloads: 10 This Week
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  • 2

    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/EigenLab). ...
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  • 3

    Muninn

    A software package for estimating generalized ensemble weights in Mark

    We have moved to GitHub: https://github.com/muninnorg/muninn As of July 2015 we have moved to GitHub. The SourceForge page will preserved, with the old releases. Muninn is a software package for estimating generalized ensemble weights in Markov chain Monte Carlo (MCMC) simulations. The method is full automated and makes use of the generalized multihistogram (GMH) equations for estimation the density of states [1]. The package is implemented in C++ and has a convenient interface.
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  • 4
    The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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  • 5
    Algorithms to estimate the number of k-colorings of a low-degree graph using samplings from a markov chain are provided.
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  • 6
    A script to generate random text using Markov chains. Output text will be stylistically similar to the training text.
    Downloads: 0 This Week
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  • 7
    Two Markov-Chain-Monte-Carlo-algorithms ("Glauber Dynamics" and "Flip Dynamics") used for randomly sampling k-colorings of a graph.
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