Search Results for "bayesian" - Page 3
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Showing 177 open source projects for "bayesian"
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MultiBUGS-MSNBurr-IIa
MultiBUGS after the addition of the MSNBurr-IIa distribution module
MultiBUGS is a statistical software for bayesian analysis developed by BUGS. This program uses source-code from https://www.multibugs.org/ and the addition of modules is intended as one of the research in the thesis. The added module is the MSNBurr-IIa distribution which is a neonormal distribution to accommodate symmetrical and right-skewed data. -
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auto-sklearn
Automated machine learning with scikit-learn
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself and contains a multitude of improvements which speed up the fitting the AutoML system. auto-sklearn 2.0 works the same way as regular auto-sklearn. auto-sklearn is licensed the same way as scikit-learn, namely the 3-clause BSD license. -
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Pattern Recognition and Machine Learning
Repository of notes, code and notebooks in Python
...Each section of the repository corresponds to chapters in the book and includes code examples that demonstrate statistical modeling, machine learning methods, and Bayesian inference techniques. These notebooks provide visualizations and computational demonstrations that help clarify complex topics such as probabilistic models, neural networks, kernel methods, and graphical models. The repository also includes implementations of sampling methods, clustering algorithms, and dimensionality reduction techniques used throughout machine learning research. -
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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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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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GENET-CNV
Integrated DNA copy number variation and gene expression analysis
The Boolean implication networks outperformed Bayesian networks, Pearson’s correlation networks, and other Boolean networks in constructing genome-scale co-expression networks evaluated with comprehensive biological pathways and Gene Ontology in MSigDB. References: Guo NL, Wan YW. Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival. -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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vbtrack
Divide single particle tracks into Brownian and motor-driven intervals
vbtrack is a software package for dividing organelle or particle tracks into Brownian and motor-driven intervals by variational maximization of the Bayesian evidence. Either particle velocity or directional persistence can be used to detect the number of states and the characteristics of each state at each frame of a particle track. -
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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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BayesianOptimization.jl
Bayesian optimization for Julia
Bayesian optimization for Julia. -
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Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
...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. The repository organizes the material into printable chapters so that students can study the notes offline or use them as reference material while learning machine learning theory. -
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Bayesian machine learning notebooks
Notebooks about Bayesian methods for machine learning
Notebooks about Bayesian methods for machine learning. -
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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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Kalman and Bayesian Filters in Python
Kalman Filter book using Jupyter Notebook
...Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn? This book teaches you how to solve all sorts of filtering problems. Use many different algorithms, all based on Bayesian probability. In simple terms Bayesian probability determines what is likely to be true based on past information. ... -
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Think Bayes
Code repository for Think Bayes
ThinkBayes is the code repository accompanying Think Bayes: a book on Bayesian statistics written in a computational style. Instead of heavy focus on continuous mathematics or calculus, the book emphasizes learning Bayesian inference by writing Python programs. The project includes code examples, scripts, and environments that correspond to the chapters of the book. Learners can run the code, experiment with probability distributions, compute posterior probabilities, and understand Bayesian updating via simulation and algorithmic methods. ... -
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HMMBase.jl
Hidden Markov Models for Julia
HMMBase is not maintained anymore. It will keep being available as a Julia package but we encourage existing and new users to migrate to HiddenMarkovModels.jl which offers a similar interface. For more information see HiddenMarkovModels.jl: when did HMMs get so fast?. HMMBase provides a lightweight and efficient abstraction for hidden Markov models in Julia. Most HMMs libraries only support discrete (e.g. categorical) or Normal distributions. In contrast HMMBase builds upon Distributions.jl... -
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BayesFM-MCMC-V1.0
A package for fine mapping causitive variants
A new Bayesian MCMC method for fine mapping causitive variants within GWAS identified region for complex traits. The small region is typically across 1Mb. The feature of the package is that (1) it identify multiple causitive variants (credible set) simultaneousely; (2) it generates credible set containing highly linked variants rather in addition to a lead variants for each signal. -
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MSIGNET
A Bayesian approach for disease-associated gene network identification
MSIGNET integrates disease-specific gene expression data and human protein-protein interactions in a Bayesian network, and identifies interactions of genes significantly expressed under the disease condition. -
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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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Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures! Optimization courses which form the foundation for ML, DL, RL. Computer Vision courses which are DL & ML heavy. Speech recognition courses which are DL heavy. Structured Courses on Geometric, Graph Neural Networks. Section on Autonomous Vehicles. Section on Computer Graphics with ML/DL focus. -
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We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors. The estimated protein clusters form regulatory modules in different chromatin states, which help explain how proteins work together in regulating gene expression. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. ...
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Edward
A probabilistic programming language in TensorFlow
...It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. Expectation-Maximization, pseudo-marginal and ABC methods, and message passing algorithms. -
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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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CABBaGe
Classification Algorithm Based on a Bayesian method for Genomics
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BisSNP
Bisulfite-seq/NOMe-seq SNPs & cytosine methylation caller
Now in Github: https://github.com/dnaase/Bis-tools/tree/master/Bis-SNP BisSNP is a package based on the Genome Analysis Toolkit (GATK) map-reduce framework for genotyping in bisulfite treated massively parallel sequencing (Bisulfite-seq, NOMe-seq and RRBS) on Illumina platform. It uses bayesian inference with either manually specified or automatically estimated methylation probabilities of different cytosine context(not only CpG, CHH, CHG in Bisulfite-seq, but also GCH et.al. in other bisulfite treated sequencing) to determine genotypes and methylation levels simultaneously. It works for both of single-end and paired-end reads.Specificity and sensitivity has been validate by Illumina IM SNP array. ...
