david-dunson

-Efficient-Manifold-Learning-Using-Spherelets

This project provides codes for manifold learning using spherelets (see https://arxiv.org/abs/1706.08263 )

bnphomc

Bayesian Nonparametric Modeling of Higher Order Markov Chains

divide-conquer-bayes

WASP and PIE algorithms of Sanvesh Srivastava, Cheng Li, and David Dunson (2015, 2017)

Fisher_Gaussian_Kernel_Mixture

Density estimation using Fisher-Gaussian (FG) kernel mixture approach

gaussian-copula-factor-model

"Bayesian Gaussian Copula Factor Models for Mixed Data" by Jared S. Murray, David B. Dunson, Lawrence Carin, Joseph E. Lucas. This is a read-only mirror of the CRAN R package repository.

GeodesicDistance

Misc

Presentations, lectures, other source code

nestedGP

"Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes" (2012) by Bin Zhu, David B. Dunson

SPAclassifier

Spherical Approximation Classifier

sparse_bayesian_infinite_factor_models

Sparse Bayesian Infinite Factor Models

david-dunson/sparse_bayesian_infinite_factor_models

Sparse Bayesian Infinite Factor Models

david-dunson/GeodesicDistance

david-dunson/-Efficient-Manifold-Learning-Using-Spherelets

This project provides codes for manifold learning using spherelets (see https://arxiv.org/abs/1706.08263 )

david-dunson/SPAclassifier

Spherical Approximation Classifier

david-dunson/bnphomc

Bayesian Nonparametric Modeling of Higher Order Markov Chains

david-dunson/Fisher_Gaussian_Kernel_Mixture

Density estimation using Fisher-Gaussian (FG) kernel mixture approach

david-dunson/Misc

Presentations, lectures, other source code

david-dunson/generalized-infinite-factorization-models

This repository contains code to implement the methods from the paper Schiavon, Canale and Dunson (2022), "Generalized infinite factorization models", Biometrika 109 (3), 817-835. This article proposes a novel class of structured Bayesian latent factor models which allow one to include "meta features" providing information on the different measured variables; such features can inform about the dependence structure among the variables.

david-dunson/Hierarchical-Model-Notes

david-dunson/infinitefactor

Bayesian infinite factor modelling

david-dunson/Multivariate-mixed-membership-models

This paper provides code from the paper: Russo M, Singer BH, Dunson DB (2022) Multivariate mixed membership models: Inferring domain-specific risk profiles. The Annals of Applied Statistics 16 (1), 391-413. This method generalizes mixed membership models to allow different membership scores for different types of variables.

david-dunson/TARP

Targeted Random Projection

david-dunson/Bayes-nonparametric-taxonomic-classification

Contains code from the paper: Zito A, Rigon T, Dunson DB (2022) "Inferring taxonomic placement from DNA barcoding allowing discovery of new species" arXiv:2201.09782. DNA barcoding is conducted on field samples and it is important to classify the samples taxonomically, while allowing discovery of new taxa; these may be organisms unknown to science or ones known to science by lacking a reference sequence. BayesANT allows the current taxonomy from the reference database to grow probabilistically as new DNA barcoding data are collected. This work was motivated by our Lifeplan project funded by the ERC.

david-dunson/bayesian-conditional-tensor-factorization

Bayesian Conditional Tensor Factorization

david-dunson/Bayesian-species-sampling-methods

This paper proposes a new class of Bayesian species sampling models motivated by DNA barcoding data. The fundamental problem is predicting how many new OTUs will be present in some additional number of sequences based on partial sequencing information. All the biological sample can't be sequenced due to expense issues so this allows inference on how many OTUs are in the sample based on limited sequencing depth.

david-dunson/BayesianPyramids

Matlab code for the paper Gu, Y. and Dunson, D.B. (2021), Identifying Interpretable Discrete Latent Structures from Discrete Data.

david-dunson/BC_tSNE

Bacth corrected t-SNE - Aliverti, Wilhelmsen and Dunson

david-dunson/bmms

Bayesian Modular and Multiscale Regression

david-dunson/D-probability

"Framework for Probabilistic Inferences from Imperfect Models" by Meng Li, David B. Dunson

david-dunson/david-dunson.github.io

david-dunson/Gaussian-process-subspace-regression

This contains an R package for implementing the methods in Zhang R, Mak S, Dunson DB (2022) Gaussian process subspace prediction for model reduction. SIAM Journal on Scientific Computing 44 (3), A1428-A1449

david-dunson/long-memory-models-for-binary-time-series

This repository contains code from the paper Chakraborty A, Ovaskainen O, Dunson DB (2022) "Bayesian semi parametric long memory models for discretized event data", Annals of Applied Statistics 16 (3), 1380-1399. The paper proposes a novel fractional Brownian probit model for binary time series with long memory motivated by ecological applications to bird species monitoring.

david-dunson/lxsplines

Bayesian Local Extremum Splines

david-dunson/mills

Composite mixture of log-linear models for categorical data

david-dunson/Mills-model-for-categorical-data

This repository contains the code from Aliverti A, Dunson D (2022) Composite mixture of log-linear models with application to psychiatric studies. Annals of Applied Statistics 16 (2), 765. The proposed "Mills" model is designed to improve modeling of multivariate categorical data, bridging between latent class models and log-linear models.

david-dunson/MMM-tutorial

david-dunson/Nearly-orthogonal-GPs

david-dunson/Scalable-Bayesian-spatial-modeling-for-general-model-structures-beyond-Gaussian-

This contains code for implementing the methods in Peruzzi M, Dunson DB (2022) "Spatial meshing for general Bayesian multivariate models" arXiv:2201.10080. The methodology is designed to provide scalable algorithms for large spatial datasets including for models not covered by many of the previous scalable Bayesian methods.

david-dunson/SOG

Removing the influence of a group variable in high-dimensional predictive modelling

david-dunson/Spatial-multivariate-trees

This contains code for implementing the methods in Peruzzi M, Dunson DB (2022) Spatial Multivariate Trees for Big Data Bayesian Regression. Journal of Machine Learning Research 23, 17:1-17:40