High Quality Geophysical Analysis provides a general purpose Bayesian and deterministic inversion framework for various geophysical methods and spatially distributed / timeseries data
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Updated
Aug 1, 2024 - Julia
High Quality Geophysical Analysis provides a general purpose Bayesian and deterministic inversion framework for various geophysical methods and spatially distributed / timeseries data
Receiver function inversion by reversible-jump Markov-chain Monte Carlo
Bayeisan inversion to recover Green's functions of receiver-side structures from teleseismic waveforms
[Quantitative Finance 2019] Sovereign Risk Zones in Europe During and After the Debt Crisis
Statistical analysis of gene family evolution using phylogenetic birth-death processes and WGD inference using rjMCMC
A parallelization of RJMCMC. To cite this software publication: https://www.sciencedirect.com/science/article/pii/S2352711021000091
Hierarchical Bayesian approaches for robust inference in ARX models
RJMCMC: Genome-Wide Nucleosome Positioning in R
Code accompanying the paper "Microlensing model inference with normalising flows and reversible jump MCMC"
A Bayesian functional regression framework built on RKHS's and reversible jump MCMC.
Julia library for Bayesian non- and semi-parametric hazard models using B-splines
A refactoring of David Hastie's AutoMix Reversible Jump MCMC
Estimate ESS and posterior model probabilities by fitting a discrete Markov chain to output from a rjMCMC sampler
Bioconductor Package - Genome-Wide Nucleosome Positioning in R with an optimized section in C++
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