Search Results for "bayesian network"
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13 projects for "bayesian network" with 1 filter applied:
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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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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... -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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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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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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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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software for identifying modules in networks (e.g. "community detection"), as described in "a bayesian approach to network modularity" (physical review letters 100, 258701 (2008); http://link.aps.org/abstract/PRL/v100/e258701).
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The application Crimeblips provides up-to-date crime statistics for Berlin (Germany). It maps and visualizes crimes, allowing users to identify crime hot spots, trends and general patterns. Bayesian algorithms are used to extract relevant information.
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Linasf is a PHP implementation of a SpamAssassin-like spam filter. It uses Bayesian Filters, User-defined rules and Artificial Neural Networks to obtain excellent results. Training and integration with current PHP system is very easy. (uses MySQL as DB)
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This is a spam hardened Wiki. Spam protection includes no posting of URLs by public members, e-mail sign up, RSS feed of IP block list, easy ISP reporting of attacks, Bayesian IP blocking, random submit fields and Google rel attributes.
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Java/XML toolkit for research using Bayesian networks and other graphical models of probability (exact and approximate inference, structure learning, etc.)
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RISO: distributed belief networks
Distributed, heterogeneous Bayesian belief networks
RISO: distributed, heterogeneous Bayesian belief networks. Belief network: a probability model defined on an acyclic directed graph; distributed: nodes can be on different hosts; and heterogeneous: allowing different types of conditional distributions.
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