Markov Chains and Monte Carlo Methods

No-U-Turn sampling for phylogenetic trees

This paper introduces a new Bayesian inference algorithm for phylogenetic trees using No-U-Turn sampling, which converges faster and explores the posterior distribution more efficiently than traditional methods.

Likelihood-free nested sampling for biochemical reaction networks

This paper introduces a likelihood-free nested sampling method for Bayesian inference in stochastic biochemical reaction networks, providing an unbiased evidence estimator and posterior samples with a novel termination criterion.

A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology

This paper introduces a Bayesian method for nonlinear model calibration using omics data, showing it outperforms existing methods in robustness and computational efficiency.

Efficient Exact Inference for Dynamical Systems with Noisy Measurements using Sequential Approximate Bayesian Computation

The paper introduces an efficient algorithm for ABC that accounts for measurement noise, improving parameter estimation accuracy in dynamical systems with noisy data.

GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

GpABC is a Julia package that implements ABC methods with Gaussian process emulation for parameter inference and model selection in systems biology models.

Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics

This study compares variational inference and MCMC methods for phylogenetic inference using Stan, finding variational methods faster but less accurate.

Tunings for leapfrog integration of Hamiltonian Monte Carlo for estimating genetic parameters

This study applies Hamiltonian Monte Carlo to animal breeding data, identifies optimal leapfrog integration parameters, and shows superior performance over Gibbs sampling in models using pedigree or genomic information.

Guided proposals for efficient weighted stochastic simulation

The paper introduces an importance density for importance sampling to estimate rare event probabilities in biochemical systems, showing improved performance over existing methods.

Multi-level Approximate Bayesian Computation

The paper introduces a multi-level approximate Bayesian computation framework for inference in mechanistic models of biological systems.

Thermodynamic integration for dynamic causal models

This paper introduces thermodynamic integration for dynamic causal modeling, showing it provides more robust log evidence estimates than variational Bayes in complex models.

Inverse sensitivity analysis of mathematical models avoiding the curse of dimensionality

The paper introduces a novel Markov Chain Monte Carlo method for inverse sensitivity analysis of deterministic biological models, demonstrating its application to several mathematical models.

pyABC: distributed, likelihood-free inference

pyABC provides a distributed and scalable framework for likelihood-free inference using ABC-SMC, enabling efficient computation and customization for complex models.

Effective Online Bayesian Phylogenetics Via Sequential Monte Carlo With Guided Proposals

This paper develops and evaluates online Bayesian phylogenetic inference methods using sequential Monte Carlo with guided proposals, showing reduced computation time compared to MCMC-based methods without significant accuracy loss.