Search Results for "bayesian python" - Page 2

BayesRate is a program to estimate speciation and extinction rates from dated phylogenies in a Bayesian framework. The methods are described in: Silvestro, D., Schnitzler, J. and Zizka, G. (2011) A Bayesian framework to estimate diversification rates and their variation through time and space. BMC Evolutionary Biology, 11, 311 Silvestro D., Zizka G. & Schulte K. (2014) Disentangling the effects of key innovations on the diversification of Bromelioideae (Bromeliaceae). Evolution, 68, 163-175.

PyRate is a Python program to estimate speciation, extinction, and preservation rates from fossil occurrence data using a Bayesian framework. The method was described by D Silvestro, J Schnitzler, LH Liow, A Antonelli, and N Salamin in Systematic Biology (http://sysbio.oxfordjournals.org/content/early/2014/02/08/sysbio.syu006.abstract). *Please download the most up-to-date code from the "PyRate code" tab on this page or from: https://github.com/dsilvestro/PyRate * *An updated manual can be found here: https://github.com/dsilvestro/PyRate/tree/master/tutorials *

RadicalSpam Virtual Appliance takes full solution of RadicalSpam Community Edition , pre-installed in a OVF virtual machine ( Open Virtual Format ) compatible with the best virtualization platforms on the market , including VMware ESX Server. More information : http://www.radical-spam.org

A brief summary of the main features of Fesslix: - Perform non-intrusive reliability analysis or Bayesian updating either --- by running commands on the command line or --- by means of an Octave interface or --- by means of a Python interface - Flexible input language for writing Fesslix parameter files --- Control flow statements (e.g. if, for, while) --- Most parameters can be defined as functions - Working with response surfaces - Linear finite element analysis using truss, beam and plane stress/strain elements - Spectral Stochastic Finite Elements - Bayesian networks This is the download page for Windows executables of Fesslix.

Provide a reference implementation of Moving Taylor Bayesian Regression, a method for nonparametric multi-dimensional function estimation with correlated errors from finite samples, as a Python package based on SciPy

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.

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

KolmoGNUS is a simple yet powerful program which recommends links to the user based on his tastes. Links are taken from selected del.icio.us feeds, and user tastes are learned with a bayesian inference technique.

Feedisto is your personal newspaper. Your newspaper is collocted from rss feeds, parsed by a bayesian filter to rate its relevance and served as a static html page or on a webserver to train the filter. Writing plugins is very easy.

Bayesian anti-spam classifier written in Python.

A python/C++ framework for Bayesian phylogenetic analysis.

Python Web Service that reads rss feeds and, using bayesian stats, reports how much you will like the articles.

Reverned is a general purpose Bayesian classifier written in Python. It is designed to be easily extended to any application domain.

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.

You can filter your email using a bayesian like statistical method. Written in 100% Python, easily extensible, and is used as a pop3 proxy to reduce the intrusion on your system.

Pyndex is a simple and fast full-text indexer and Bayesian classifier implemented in Python. It uses Metakit as its storage back-end. It works well for quickly adding search to an application, and is also well suited to in-memory indexing and search.