Search Results for "bayesian" - Page 5
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Showing 230 open source projects for "bayesian"
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vbTPM
Variational Bayes for tethered particle motion
.../gku563 2014-06-11: manuscript accepted for publication in Nucleic Acids Research. Preprint: http://arxiv.org/abs/1402.0894 If you use this code, please cite our work: Stephanie Johnson, Jan-Willem van de Meent, Rob Phillips, Chris H. Wiggins, and Martin Lindén Multiple LacI-mediated loops revealed by Bayesian statistics and tethered particle motion. Nucleic Acids Research (2014), doi: 10.1093/nar/gku563 -
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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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We developed a novel mining pipeline, inGAP, which is guided by a Bayesian principle to detect single nucleotide polymorphisms, insertion and deletions by comparing high-throughput pyrosequencing reads with a reference genome of related organisms.
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Bycom
Bycom can do methylcytosine calling (5mC calling) from BS-seq.
Bycom can do methylcytosine calling from BS-seq (WGBS and RRBS), and either unmapped reads (FASTQ) or mapped reads (SAM/BAM) could be permitted for the input data. Certain SNPs (C>A/G) can also be selected in the output. 1. 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... -
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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... -
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StabLe
An algorithm for learning stable graphical models from data
Stable Graphical Model Learning (StabLe) is an algorithm for learning the structure and parameters of stable graphical (SG) models from data. Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. SG models are multi-variate stable distributions that represent Bayesian networks whose edges encode linear... -
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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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dbacl - digramic Bayesian classifier
commandline multiclass email and text filter
dbacl is a general purpose digramic Bayesian text classifier. It can learn text documents you provide, and then compare new input with the learned categories. It can be used for spam filtering, or within your own shell scripts. Sometimes it plays che -
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ABM-Calibration-SensitivityAnalysis
Codes and Data for Calibration and Sensitivity Analysis of ABM
... fitting: 1. Full Factorial Design 2. Simple Random Sampling 3. Latin Hypercube Sampling 4. Quasi-Newton Method 5. Simulated Annealing 6. Genetic Algorithm 7. Approximate Bayesian Computation b. Sensitivity Analysis: 1. Local SA 2. Morris Screening 3. DoE 4. Partial (Rank) Correlation Coefficient 5. Standardised (Rank) Regression Coefficient 6. Sobol' 7. eFAST 8. FANOVA Decomposition Have also a look on our other projects: http://www.uni-goettingen.de/de/315075.html -
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fesslix
Stochastic Analysis
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... -
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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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Soils: Bayesian predictive models
rJags code for Bayesian Postorior Predictive Models
All the code used for the SSSJA paper by Huzurbazar, Wick, Gasch and Stahl entitled "Bayesian Posterior Predictive Distributions for Assessing Soil Aggregation in Undisturbed Semiarid Grasslands". We also include some supplementary plots not included in the paper. -
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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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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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abc-sde
approximate Bayesian computation for stochastic differential equations
A MATLAB toolbox for approximate Bayesian computation (ABC) in stochastic differential equation models. It performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations (SDEs) and not limited to the "state-space" modelling framework. Both one- and multi-dimensional SDE systems are supported and partially observed systems are easily accommodated. Variance components for the "measurement error" affecting the data... -
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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...
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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... -
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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
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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... -
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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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ABC-DynF
Adaptive Bayesian Classifier with Dynamic Features
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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... -
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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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Averaged N-Dependence Estimators - AnDE
AnDE implements A1DE and A2DE
... Estimators. Machine Learning. 58(1):5-24 and G.I. Webb, J. Boughton, F. Zheng, K.M. Ting and H. Salem (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive {Bayesian} classification. Machine Learning. 86(2):233-272.