Search Results for "bayesian mixture model" - Page 5
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Showing 170 open source projects for "bayesian mixture model"
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BiomeNet
BAYESIAN INFERENCE OF METABOLIC DIVERGENCE AMONG MICROBIAL COMMUNITIES
...Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic networks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. ... -
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Bayesian Gaussian mixture model (GMM) fitting with noninformative priors, as described in (Stoneking, 2014 - arXiv:1405.4895). MATLAB and R implementations.
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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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Bayesian Estimated Core Genome
A pipeline for estimating the core genome of a set of genome sequences
...BIGSdb: Scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics 2010, 11:595. 2. van Tonder AJ, Mistry S, et al.Defining the estimated core genome of bacterial populations using a Bayesian decision model. (under review) 3. Krzywinski M, Schein J et al. Circos: an information aesthetic for comparative genomics. Genome Res 2009, 19:1639-1645. -
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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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phcfM
R package for modelling anthropogenic deforestation
phcfM is an R package for modelling anthropogenic deforestation. It was named after the REDD+ pilot-project 'programme holistique de conservation des forêts à Madagascar'. phcfM includes two main functions: (i) demography(), to model the population growth with time in a hierarchical Bayesian framework using population census data and Gaussian linear mixed models and (ii) deforestation(), to model the deforestation process in a hierarchical Bayesian framework using land-cover change data and Binomial logistic regression models with variable time-intervals between land-cover observations. ... -
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Bycom
Bycom can do methylcytosine calling (5mC calling) from BS-seq.
...There's no softwares or methods identify methylcytosines considering the cell heterozygosis caused by multicellular sequencing. Bycom introduced it along with the sequencing errors and unconverson rate based on the Bayesian model. 2. Several parameters in Bycom could be set as what the users want to, such as depth threshold, quality control value, conversion rate, processor number. 3. Bycom do the mapping based on BSMAP, and provide the parameters using in the alignment. -
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AMICA
Adaptive Mixture Independent Component Analysis with Shared Components
Multiple mixture ICA, where model means and independent components are learned. It is possible to reject data and share component between models to increase efficiency. Matlab and independent gui (using Qt) nterfaces available. Uses MPI and OpenMP for inter-node and intra-node parallelization. -
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StabLe
An algorithm for learning stable graphical models from data
...SG models are multi-variate stable distributions that represent Bayesian networks whose edges encode linear dependencies amongst random variables. A preprint version of the manuscript describing stable graphical models is available at http://arxiv.org/abs/1404.4351. -
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msBayes allows complex and flexible phylogeographic inference. More specifically, you can test the simultaneous divergence (TSD) of multiple population (species) pairs. It uses approximate Bayesian computation (ABC) under a hierarchical model.
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ABM-Calibration-SensitivityAnalysis
Codes and Data for Calibration and Sensitivity Analysis of ABM
Find here the model, code, and example results of parameter fitting/calibration and sensitivity analysis for an agent-based model using NetLogo and R. The corresponding manuscript is published in Journal of Artificial Societies and Social Simulation as: Thiele JC, Kurth W, Grimm V (2014): Facilitating parameter estimation and sensitivity analysis of agent-based models: a cookbook using NetLogo and R. -
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ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes
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MCTIMME
Microbial Counts Trajectories Infinite Mixture Model Engine
MCTIMME is a nonparametric Bayesian computational framework for analyzing microbial time-series data.The current implementation is in Matlab. -
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Fast Matrix for Java
General purpose matrix utilities for Java in Parallel Computing
Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors. -
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GenoTan identifies inherited variation of microsatellite loci from short sequence reads using a discretized Gaussian mixture model combined with a rules-based approach.
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High Frequency Based Volatility Modeling
A GNU C and Java High Frequency Volatility Modeling Toolkit
A c library with a wrapper written in java for modeling high-frequency based volatility (HEAVY). The model is described in full detail by Shephard and Sheppard in http://www.nuff.ox.ac.uk/users/shephard/papers/heavy.pdf. Access to functions for forecasting volatility, distribution analysis, and Bayesian estimation are also available. Many of the features have been tested and seem to work. Bugs and breakdowns are always inevitable and the package will continuously be updated in the future as improvements are made. -
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GSP program are based on Bayesian framework with an EM algorithm to predict genome size iteratively, which is elegant in mathematics. The model first develop under the no sequencing error model, then extend to the sequencing errors containing model.
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InferRho2
Fine-scale recombination inference from population genomic data.
InferRho2 is a MCMC based program that jointly estimates 3 recombination parameters, the population crossing-over rate, the population gene-conversion rate and the mean conversion tract length from population genomic datasets under a Bayesian framework. It uses a full-likelihood method to infer the posterior distribution of recombination rates along the sequence under a variable recombination rate model that includes hotspots. The ratio of gene-conversion to crossing-over rates can take 2 possible values f1 and f2 within hotspots and non-hotspots respectively. The program outputs the posterior distribution of f1, f2 and the 3 recombination parameters for marker intervals in the .rho output files. ... -
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Adaptative thresholding for fMRI
Matlab scripts to perform cluster FDR adaptative thresholding
Matlab plug-in for SPM allowing to obtain a threshold for cluster FDR - the method fits a Gamma-Gaussian mixture model to the SPM-T and finds the optimal threshold (crossing between noise and activation). Optionally write the thresholded maps. -
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The Automated Parameter Estimation and Model Selection Toolkit is a fast, parallelized MCMC engine written in C for Bayesian inference (parameter estimation and model selection).
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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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GMM-GMR is a light package of functions in C/C++ to compute Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). It allows to encode any dataset in a GMM, and GMR can then be used to retrieve partial data by specifying the desired inputs.
