Search Results for "markov"
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Showing 6 open source projects for "markov"
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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.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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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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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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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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A script to generate random text using Markov chains. Output text will be stylistically similar to the training text.
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Two Markov-Chain-Monte-Carlo-algorithms ("Glauber Dynamics" and "Flip Dynamics") used for randomly sampling k-colorings of a graph.
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