Search Results for "markov"
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Showing 17 open source projects for "markov"
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
...The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). Vector autoregressive models, VAR and structural VAR. Vector error correction model, VECM. Robust linear models with support for several M-estimators. statsmodels supports specifying models using R-style formulas and pandas DataFrames. -
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Meridian
Meridian is an MMM framework
...The framework provides a robust foundation for constructing in-house MMM pipelines capable of handling both national and geo-level data, with built-in support for calibration using experimental data or prior knowledge. Meridian uses the No-U-Turn Sampler (NUTS) for Markov Chain Monte Carlo (MCMC) sampling to produce statistically rigorous results, and it includes GPU acceleration to significantly reduce computation time. -
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Introduction-NLP
Detailed notes on HanLP's new book, "Introduction to Natural Language"
...Later chapters cover part-of-speech tagging, named entity recognition, information extraction, text clustering, text classification, dependency parsing, and deep learning. Algorithms discussed include hidden Markov models, perceptrons, conditional random fields, Naive Bayes, and support vector machines. Many chapters link to executable code examples and datasets for hands-on study. The repository is designed to help learners organize the book’s key ideas into a practical reference for Chinese NLP work. -
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Gen.jl
A general-purpose probabilistic programming system
...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 for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. ... -
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JAGS is Just Another Gibbs Sampler. It is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo.
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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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MarkovJunior
Probabilistic language based on pattern matching
The MarkovJunior project is a procedural generation engine that uses rule-based systems inspired by Markov processes to create complex structures and patterns. It allows users to define transformation rules that iteratively modify a grid or space, producing outputs such as textures, maps, and architectural layouts. The system is highly flexible, supporting both deterministic and probabilistic rule application to generate diverse results. -
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Markovify
A simple, extensible Markov chain generator
Markovify is an open-source Python library for generating random text using Markov chain models trained on existing writing. It analyzes large text collections to identify word sequences and their statistical relationships. The software uses these patterns to construct new sentences that resemble the vocabulary and structure of the original material. Users can customize the number of preceding words considered during generation to influence coherence and variation. -
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Probability Cheatsheet
A comprehensive 10-page probability cheatsheet
...It likely includes definitions of random variables, PMFs and PDFs, expectations, variance, common distributions (e.g. binomial, normal, Poisson, exponential), conditional probability, Bayes’ theorem, moment generating functions, and perhaps important inequalities (Markov, Chebyshev, Chernoff). The cheat sheet is intended as a quick reference for students, data scientists, statisticians, or anyone needing to recall core probability formulas without diving into textbooks. It may include visual diagrams (e.g. distributions’ shapes), tips or mnemonic notes, and examples of application (e.g. computing probabilities or expectations). ... -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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Logi (Local Observations to Global Integration) transforms several local coordinate systems (composed of polar measurements) into a global spatial uniform network with respect to the Guide to the Expression of Uncertainty in Measurement (GUM).
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High-order HMM in Matlab
Implementation of duration high-order hidden Markov model in Matlab.
Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition. -
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T3S Tool
Learning Stochastic Discrete Event Systems
...However, models that cannot be constructed with an hand-made process need to be learned. Thus, the SDES toolbox proposes an automated solution that is embedded in Matlab to learning and analisis generalized semi-Markov processes. -
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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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Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.
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The Markovski project implements an interpreter/compiler for a language named Markovski, used to write Markov algorithms (see http://en.wikipedia.org/wiki/Markov_algorithm).
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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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