Search Results for "bayesian network"
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Showing 42 open source projects for "bayesian network"
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. -
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Anti-Spam SMTP Proxy Server
Anti-Spam SMTP Proxy Server implements multiple spam filters
The Anti-Spam SMTP Proxy (ASSP) Server project aims to create an open source platform-independent SMTP Proxy server which implements auto-whitelists, self learning Hidden-Markov-Model and/or Bayesian, Greylisting, DNSBL, DNSWL, URIBL, SPF, SRS, Backscatter, Virus scanning, attachment blocking, Senderbase and multiple other filter methods. Click 'Files' to download the professional version 2.8.1 build 24261. A linux(ubuntu 20.04 LTS) and a freeBSD 12.2 based ready to run OVA of ASSP V2 are... -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels,... -
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MSIGNET
A Bayesian approach for disease-associated gene network identification
MSIGNET integrates disease-specific gene expression data and human protein-protein interactions in a Bayesian network, and identifies interactions of genes significantly expressed under the disease condition. -
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The Neural Process Family
This repository contains notebook implementations
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from... -
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BNMCMC
Infer Bayesian Network Structure from data using MCMC methods
Infer Bayesian Network Structure from data using MCMC methods -
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The Interaction Network Ontology (INO) is an ontology in the domain of interaction networks. INO develoment follows the OBO Foundry principles. INO can be used for automated reasoning such as literature mining and Bayesian network reasoning.
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Mocapy++ is a Dynamic Bayesian Network toolkit, implemented in C++. It supports discrete, multinomial, Gaussian, Kent, Von Mises and Poisson nodes. Inference and learning is done by Gibbs sampling/Stochastic-EM.
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Cognitive Stability and Flexibility FIT
GPU Accelerated Fitting of Behavioral Data by a Physiological Model
Using the NVIDIA-CUDA framework, mcmc_min is able to efficiently sample the Bayesian posterior distribution over the parameters of a physiologically derived model of a task-switching and distractor inhibition paradigm. The model features a working-memory module, implementing the currently active task rule in terms of a two-dimensional stochastic dynamical system with three attractor states (rule 1, rule 2, spontaneous). -
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FamSeq
Variant calling on the basis of pedigree information
...FamSeq accommodates de novo mutations and can perform variant calling at chromosome X. To accommodate variations in data complexity, FamSeq consists of three distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, Elston-Stewart algorithm and Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA® graphics processing unit. -
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BBNanalysis
Bayesian Belief Network Analysis & Validation
A tool for analysis of Bayesian Belief Networks/Decision Networks in Genie 2.0 (.xdsl) format. Developed as a part of the HELICOPTER project (http://www.helicopter-aal.eu). -
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Bayesian Network tools in Java (BNJ) is an open-source suite of software tools for research and development using graphical models of probability. It is published by the Kansas State University Laboratory for Knowledge Discovery in Databases (KDD).
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A QGIS plugin to facilitate data processing for Bayesian spatial modeling. (doi: 10.1111/j.1600-0587.2010.06598.x) Alternative site: http://code.google.com/p/maps2winbugsplugin/
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AdPreqFr4SL
Adaptive Prequential Learning Framework
The AdPreqFr4SL learning framework for Bayesian Network Classifiers is designed to handle the cost / performance trade-off and cope with concept drift. Our strategy for incorporating new data is based on bias management and gradual adaptation. Starting with the simple Naive Bayes, we scale up the complexity by gradually updating attributes and structure. Since updating the structure is a costly task, we use new data to primarily adapt the parameters and only if this is really necessary, do we adapt the structure. ... -
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imDEV
Tools for multivariate data visualization, exploration and analysis.
You may also like some newer work: http://createdatasol.com/ Project imDEV is an application of RExcel, which seamlessly integrates Excel and R for tasks focused on multivariate data visualization, exploration, and analysis. Interactive modules for dimensional reduction (imPCA), prediction (imPLS), feature selection, analysis of correlation (imCorrelations) and generation of networked structures (imGraph) provide an integrated environment for systems level analysis of multivariate data. -
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A general purpose Bayesian Network Toolbox. This project seeks to take advantage of Python's best of both worlds style and create a package that is easy to use, easy to add on to, yet fast enough for real world use.
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jBNC is a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.
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Open Bayes is a python free/open library that allows users to easily create a bayesian network and perform inference/learning on it. It is mainly inspired from the Bayes Net Toolbox (BNT) but uses python as a base language. www.openbayes.org
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Expediting cancer genetic and neurogenetic discovery through Bayesian Network Analysis of microarray data. Designed for genetics researchers, this takes in raw data (and a very small about of user input) and outputs reports usable by biologists.
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Presumption of probability using the Bayesian-Network(Belief propagation base). We are making product for forecasts the user's behavior from user's data. Ex. For web recommendation.
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This is a C++ Bayesian network library, including support for inference, adding evidence, parameter learning, structure learning, and decision networks. There is an associated graphical interface called 'conductor'.
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BASILISK is a probabilistic model of the conformational space of amino acid side chains in proteins. Unlike rotamer libraries, BASILISK models the chi angles in continuous space, including the influence of the protein's backbone.
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This is a reference implementation of snoBAC, a Bayesian Classifier designed to predict box H/ACA snoRNAs in Caenorhabditis nematode genomes. For details of algorithm and data, see Wang and Ruvinsky (2009) RNA in press.
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