Search Results for "markov"

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

Visual Figaro is an integrated environment to develop component libraries for the dependability modeling tool KB3 used by EDF (http://www.edf.fr/recherche/codes-de-calcul/kb3). KB3 is based on the FIGARO reliability modeling language, suitable for describing CTMC (continuous time Markov chains), DES (discrete event simulation) models, and for automatic generation of fault trees. FIGARO is objet oriented, close to natural language and enables building libraries of reusable components (knowledge bases). Go to "Files" tab to download various Figaro based tools (Visual Figaro, KB3, YAMS), lots of examples and documentation.

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). ...

Open source software for Bayesian statistics using Markov Chain Monte Carlo simulation.

Small console application running Markov algorithms. Небольшое консольное приложение, выполнающее алгоритмы Маркова.

QHQ++ is a C++ library includes: QHQc++ (C++ Numerical Library), QHQmcmc++ (Markov Chain Monte Carlo C++ Library, Bayesian Statistics), QHQsv++ (Stochastic Volatility C++ Library, Finance), QHQyc++ (Yield Curve Modeling C++ Library, Finance).

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.

Algorithms to estimate the number of k-colorings of a low-degree graph using samplings from a markov chain are provided.

Two Markov-Chain-Monte-Carlo-algorithms ("Glauber Dynamics" and "Flip Dynamics") used for randomly sampling k-colorings of a graph.