Search Results for "bayesian mixture model" - Page 4
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Showing 170 open source projects for "bayesian mixture model"
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Wheefun Options Parsing Library
Portable command line options parsing
This library provides portable command line parsing which can be used across several platforms to provide a consistent command line interface. WFOPT uses the model-view-controller pattern: options are specified via an option set, which are interpreted via a parser object and whose behavior can be modified using controller objects. This library is object-oriented and interaction occurs with it at a high level. Programmers can easily write their own parsers if none of the included parsers fit... -
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Twinify
Privacy-preserving generation of a synthetic twin to a data set
twinify is a software package for the privacy-preserving generation of a synthetic twin to a given sensitive tabular data set. On a high level, twinify follows the differentially private data-sharing process introduced by Jälkö et al.. Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI).... -
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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
This repository hosts the 2022 version of the Statistical Rethinking course. It contains course materials such as R scripts, notebooks, and worked examples aligned with McElreath’s textbook. The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. ... -
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TPLS
High Resolution Direct Numerical Simulation (DNS) of Two-Phase Flows
TPLS is a powerful and efficient 3D Direct Numerical Simulation (DNS) flow solver to simulate multiphase flows at unprecedented detail, speed and accuracy. This flow solver has been developed by Lennon Ó Náraigh (Mathematical Sciences, University College Dublin), Prashant Valluri (Engineering, University of Edinburgh), David Scott, Toni Collis and Iain Bethune (EPCC at the University of Edinburgh) and Peter Spelt (Université de Lyon1, Claude Bernard) under the aegis of several HECToR /... -
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rethinking
Statistical Rethinking course and book package
This R package accompanies Richard McElreath’s Statistical Rethinking (2nd edition), offering utilities to fit and compare Bayesian models using both MAP estimation (quap) and Hamiltonian Monte Carlo via RStan (ulam). It supports specifying models via explicit distributional assumptions, providing flexibility for advanced statistical workflows. -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
...This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. Separate models are trained for different speaker counts, and the largest-capacity model dynamically determines the actual number of speakers in a mixture. ... -
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vbTRACK_2D
Bayesian analysis of 2D(x,y) time series particle tracks using Matlab.
Matlab program analyzes 2D (xy) time-series data (tracks) by variaional Bayes, hidden Markov, Gaussian mixture pattern recognition pattern recognition methods. It finds the number of states, the position of each state, and assigns each time-point to its most probable specific state. -
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SMTracker (v1.5, v2.0)
A tool for analysis and visualization of single-molecule tracking data
SMTracker v2.0 is a MATLAB-based graphical user interface (GUI) for automatically quantifying, visualising and managing SMT data via five interactive panels, allowing the user to interactively explore tracking data from several conditions, movies and cells on a track-by- track basis. Diffusion parameters and motion behaviour is analysed by several methods: a) by a Gaussian mixture model ,or b) by using the cumulative probability distribution of square displacements, c) Mean-Squared displacement fits, d) by Jump Distance analysis. It also includes exploratory tools to visualise single trajectories or dynamic heat maps. -
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Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
...The project focuses on deriving formulas and explaining algorithms step by step so that learners can understand the mathematical foundations behind machine learning methods. The notes span sixteen chapters that cover a wide range of topics, including model evaluation, linear models, decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimensionality reduction, and reinforcement learning. Each section explains the theoretical principles of the algorithms and walks through derivations to help readers understand why the methods work rather than simply how to use them. ... -
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ChIP-BIT2
ChIP-BIT2 detects weak binding sites of TFs or HMs.
...ChIP-BIT2 is an extended version of the ChIP-BIT method: a method designed mainly for detecting narrow peaks in promoter regions as described in the following paper: Xi Chen et al., "ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles", Nucleic Acids Res (2016) 44 (7): e65. -
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FRUITS
Food Reconstruction Using Isotopic Transferred Signals
Description IMPORTANT NOTICE: The current release of FRUITS (FRUITS 3.1) will be the last as a Windows installer. This version is the closest to the soon to be released R-based ReSources package as part of the IsoMemo initiative. FRUITS is a Bayesian mixing model for diet reconstruction using isotopic data. -
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The Neural Process Family
This repository contains notebook implementations
...They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
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The JAGS ALCOVE module is an extension for JAGS, which provides functions to enable a bayesian analysis with the ALCOVE model. See the README in Code for usage details.
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GENSENG
GENSENG is a software detecting CNVs from NGS data
GENSENG is a software detecting CNVs(Copy Number Variations) from NGS(Next Generation Sequencing) data. We have uploaded the data preparation codes. Please download it from Files folder. -
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Meta-DTA
Bayesian models, meta-analysis, statistical software, critical analysi
Generally, when performing a meta-analysis of diagnostic tests, summary measures, such as sensitivity, specificity, and diagnostic odds ratios, are employed. However, they may not be adequate to integrate studies of diseases with varying prevalence . On the other hand, the cutoff points of the different studies are often unknown or heterogeneous, which is an added problem while using the abovementioned summary measures because they depend on a direct form of the cutoff points of cutused .... -
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EBprot
Bayesian Analysis of labeling-based Quantitative Proteomics Data
EBprot implements a hierarchical mixture model directly representing the peptide-protein map. The model fit is used to score differential expression of proteins in a (signed) probability scale based on reproducible quantification over multiple peptides. The package takes as input a data matrix with each row containing the data for one peptide, including protein and peptide identifier and peptide ratio(s), and returns the protein-level score table with PPscore, BFDR, Posterior Odds score, and flag for each comparison. -
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EBprotV2
Bayesian Analysis of labeling-based Quantitative Proteomics Data
EBprotV2 is an extension to the existing EBprot (version 1) which adopts a more flexible non-parametric mixture model. It serves as an differential expression analysis tool which takes into account the direct peptide to protein hierarchy which differs from the usual analysis which occurs at the protein-summary level. The tool is implemented in C++ which minimize computational time and also, incorporates an additional module which help users to generate comparison ratios between groups with multiple samples. -
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BNP-Homology
Alignment data for Protein Remote Homology Search
Alignment data from the application of a Bayesian mixture model to protein remote homology detection. -
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Pathoscope
Predicts strains of genomes in Nextgen seq alignment file (sam/bl8)
This page is here for archival purpose. Please visit github for the latest version of the software: https://github.com/pathoscope PathoScope takes next-generation sequencing reads from a mixture sample and predicts which genomes are present. We use a Bayesian framework combined with an initial reference-based alignment to assign reads to the correct genome of origin. Pathoscope 2.0: Wiki: http://sourceforge.net/p/pathoscope/wiki/Home/ Tutorial: http://sourceforge.net/projects/pathoscope/files/pathoscope2.0_v0.02_tutorial.pdf Clinical Pathoscope version: http://sourceforge.net/p/pathoscope/wiki/clinical_pathoscope/ PathoQC download: http://sourceforge.net/projects/pathoscope/files/pathoqc_v0.1.4.tar.gz/download Publications: http://www.microbiomejournal.com/content/2/1/33 http://www.biomedcentral.com/1471-2105/15/262 http://genome.cshlp.org/content/23/10/1721 Support: PS: Please send an email to mani2012 at bu dot edu if you have any questions. -
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jLDADMM
A Java package for the LDA and DMM topic models
The Java package jLDADMM is released to provide alternative choices for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. See the usage of jLDADMM in its website at http://jldadmm.sourceforge.net/ -
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BPL
Bayesian Program Learning model for one-shot learning
BPL (Bayesian Program Learning) is a MATLAB implementation of the Bayesian Program Learning framework for one-shot concept learning (especially on handwritten characters). The approach treats each concept (e.g. a character) as being generated by a probabilistic program (motor primitives, strokes, spatial relationships), and inference proceeds by fitting those generative programs to a single example, generalizing to new examples, and generating new exemplars. The repository contains code for... -
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RStan
RStan, the R interface to Stan
RStan is the R interface to Stan, a C++ library for statistical modeling and high-performance statistical computation. It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). RStan integrates with Stan’s automatic differentiation library, provides diagnostics, model comparison, posterior predictive checks, etc. ... -
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bayescount
The bayescount R package
A set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. ... -
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AMD_HealthSocialCareSim
Multi-paradigm simulation of health and social care for AMD sufferers
...This is an AnyLogic-based simulation of age-related macular degeneration (AMD), which tries to capture both the detailed operation of an eye clinic and the broader social care of AMD sufferers. It uses a mixture of agent-based modelling (ABM), discrete-event simulation (DES) and system dynamics (SD). It also serves as a demonstration simulation model for the Java Simulation Infrastructure Toolkit (JSIT), using its AnyLogic helper library, and an example of other software-engineering-best-practice ways to structure simulation models (which are covered in the journal paper "Simulation Design: Trans-Paradigm Best-Practice from Software Engineering" which introduces JSIT). ... -
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StatAlign
An extendable statistical multiple alignment tool.
...These artifacts include the dependency of the constructed phylogeny on a single (probably suboptimal) alignment and bias towards the guide tree upon which the alignment relies. The models behind the analysis permit the comparison of evolutionarily distant sequences: the TKF92 insertion-deletion model can be coupled to an arbitrary substitution model. A broad range of models for nucleotide and amino acid data is included in the package and the plug-in management system ensures that new models can be easily added.
