Projects that are tagged with bayesian networks.


Logo AMIDST Toolbox 0.4.1

by ana - April 20, 2016, 09:44:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1822 views, 248 downloads, 4 subscriptions

About: A java library for the analysis of data streams with probabilistic graphical models. AMIDST provides parallel multi-core implementations of Bayesian parameter learning algorithms using variational message passing and importance sampling for static and dynamic Bayesian networks. Additionally, AMIDST efficiently leverages existing functionalities and algorithms by interfacing to software tools such as Weka, Moa, HUGIN and R.

Changes:
  • Bugs fixed.
  • Improved usability.

Logo JMLR Jstacs 2.2

by keili - February 17, 2016, 11:57:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22180 views, 5204 downloads, 3 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

New classes and packages:

  • CorreationCoefficient: PerformanceMeasure
  • de.jstacs.clustering: package with classes for hierarchical clustering
  • DeBruijnGraphSequenceGenerator and DeBruijnSequenceGenerator for generating De Buijn sequences
  • CyclicSequenceAdaptor for representing cyclic sequences
  • PlotGeneratorResult for representing results that plot images to a Graphics2D object
  • TextResult for results that may be stored as text files
  • package de.jstacs.results.savers for generic classes that store results to disk
  • LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder for sparse local inhomogeneous mixture (Slim) models
  • PFMWrapperTrainSM for representing position frequency matrices and position weight matrices from databases
  • package de.jstacs.tools with classes for generic Jstacs tools that may be used in different user interfaces (command line, Galaxy, JavaFX)
  • Compression for ZIP compression of Strings
  • package de.jstacs.utils.graphics with generic GraphicsAdaptor using Apache XML commons
  • projects: Dimont, GeMoMa, Slim, TALEN, motif comparison

New features and improvements:

  • Major restructuring of Alignment for better efficiency
  • Alignment Costs and StringAlignment now Storable
  • New constructor of DataSet allowing a specified percentage of sequences to mismatch the given alphabet
  • BioJavaAdapter ported to BioJava 1.9
  • XMLParser now also allows for storing Sequences
  • New method for parsing HMMer profile HMMs in HMMFactory
  • Several minor improvements and bugfixes in many classes
  • Improvements of documentation of several classes

Logo Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20419 views, 4371 downloads, 3 subscriptions

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.

Changes:

Version 1.1.2d (12/29/2015):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!

Logo JMLR dlib ml 18.18

by davis685 - October 29, 2015, 01:48:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 141267 views, 23072 downloads, 4 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release has focused on build system improvements, both for the Python API and C++ builds using CMake. This includes adding a setup.py script for installing the dlib Python API as well as a make install target for installing a C++ shared library for non-Python use.


Logo JMLR libDAI 0.3.2

by jorism - July 17, 2015, 15:59:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 49309 views, 9181 downloads, 4 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.


Logo bayes scala 0.5-SNAPSHOT

by danielkorzekwa - January 9, 2015, 19:23:48 CET [ Project Homepage BibTeX Download ] 1789 views, 531 downloads, 2 subscriptions

About: It is a Scala library for building Bayesian Networks with discrete/continuous variables and running deterministic Bayesian inference

Changes:

Initial Announcement on mloss.org.


Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 36647 views, 9629 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


Logo Encog Machine Learning Framework 3.2

by jeffheaton - July 5, 2014, 23:47:06 CET [ Project Homepage BibTeX Download ] 6192 views, 2204 downloads, 1 subscription

About: Encog is a Machine Learning framework for Java, C#, Javascript and C/C++ that supports SVM's, Genetic Programming, Bayesian Networks, Hidden Markov Models and other algorithms.

Changes:

Changes for Encog 3.2:

Issue #53: Fix Out Of Range Bug In BasicMLSequenceSet. Issue #52: Unhandled exception in Encog.Util.File.ResourceLoader.CreateStream (ResourceLoader.cs) Issue #50: Concurrency bugs in PruneIncremental Issue #48: Unit Tests Failing - TestHessian Issue #46: Couple of small fixes - Temporal DataSet and SCG training Issue #45: Fixed EndMinutesStrategy to correctly evaluate ShouldStop after the specified number of minutes have elapsed. Issue #44: Encog.ML.Data.Basic.BasicMLDataPairCentroid.Add() & .Remove() Issue #43: Unit Tests Failing - Matrix not full rank Issue #42: Nuget - NuSpec Issue #36: Load Examples easier


Logo Chordalysis 1.0

by fpetitjean - March 24, 2014, 01:22:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2702 views, 606 downloads, 1 subscription

About: Log-linear analysis for high-dimensional data

Changes:

Initial Announcement on mloss.org.


About: A fast and robust learning of Bayesian networks

Changes:

Initial Announcement on mloss.org.


Logo JProGraM 13.2

by ninofreno - February 13, 2013, 20:29:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15407 views, 3036 downloads, 1 subscription

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.

Changes:

JProGraM 13.2 -- CHANGE LOG

Release date: February 13, 2012

New features: -- Support for Fiedler random graphs/random field models for large-scale networks (ninofreno.graph.fiedler package); -- Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).


Logo BRML toolbox 070711

by DavidBarber - July 17, 2011, 19:30:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 63042 views, 4530 downloads, 1 subscription

About: Bayesian Reasoning and Machine Learning toolbox

Changes:

Fixed some small bugs and updated some demos.


About: OpenGM is a free C++ template library, a command line tool and a set of MATLAB functions for optimization in higher order graphical models. Graphical models of any order and structure can be built either in C++ or in MATLAB, using simple and intuitive commands. These models can be stored in HDF5 files and subjected to state-of-the-art optimization algorithms via the OpenGM command line optimizer. All library functions can also be called directly from C++ code. OpenGM realizes the Inference Algorithm Interface (IAI), a concept that makes it easy for programmers to use their own algorithms and factor classes with OpenGM.

Changes:

Initial Announcement on mloss.org.


Logo dANN AI Library 1.3

by freemo - April 25, 2010, 05:36:46 CET [ Project Homepage BibTeX Download ] 5770 views, 1345 downloads, 1 subscription

About: dANN is an Artificial Intelligence and Artificial Genetics library targeted at employing conventional techniques as well as acting as a platform for research & development of novel techniques. As new techniques are developed and proven to be effective they will be integrated into the core library. It is currently written in Java, C++, and C#. However only the java version is currently in active development. If you want to obtain a version other than the java version you will need to get it directly from GIT.

Changes:

Please get the version in GIT only, the released version is old.


Logo JMLR pebl Python Environment for Bayesian Learning 1.0.1

by abhik - March 5, 2009, 00:05:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26582 views, 2793 downloads, 1 subscription

About: Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations.

Changes:

Updated version to 1.0.1


Logo Sleipnir 1.0

by chuttenh - June 30, 2008, 03:22:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6972 views, 1603 downloads, 1 subscription

About: The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms focusing on heterogeneous data integration and efficiency for large biological data collections.

Changes:

Initial Announcement on mloss.org.