Markov Chains and Monte Carlo Methods

abcneuralnet: Simulation-based Inference with Bayesian Neural Networks and Explainable AI for Population Genomics Inference

abcneuralnet provides a Bayesian Neural Network framework with explainable AI for population genomics inference, offering calibrated prediction intervals and uncertainty decomposition.

Efficient approximate Bayesian inference for large, heterogeneous biological datasets

The paper introduces an adaptive minibatch approach for approximate Bayesian inference, improving speed-precision tradeoffs for large, heterogeneous biological datasets.

Simulation-based inference of epidemiological and phylodynamic models via Neural Posterior Estimation

This paper applies Neural Posterior Estimation to infer parameters in epidemiological and phylodynamic models using Ebola outbreak data, showing accurate and reliable posterior estimates.

BayesForge: A Bayesian Inference library for Python, R, and Julia

BayesForge provides a unified Bayesian inference framework across Python, R, and Julia, enabling high-performance computation with hardware acceleration and demonstrating significant speedups over existing methods.

Collective Posterior Inference from Highly Variable Empirical Replicates

The paper introduces a method for fast and robust collective posterior inference from multiple noisy replicates, achieving state-of-the-art accuracy and efficiency.

Observation-masked neural posterior estimation for heterogeneous epidemiological surveillance data

The paper introduces OM-NPE, a Bayesian inference framework for heterogeneous epidemiological data, demonstrating its ability to recover parameters and uncertainties efficiently.

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

This paper shows that Cauchy perturbation kernels outperform Normal kernels in ABC-SMC by addressing overinflation and overconcentration issues, improving performance in high dimensions.

Efficient Bayesian inference for ordinary differential equation models from experimental data with uncertain measurement times

This paper presents a Bayesian framework for parameter inference in ODE models that accounts for uncertain measurement times, improving reliability and efficiency in dynamic modeling.

Composite Certainty: Addressing Metric Degeneracy in Parameter Inference for Model-Based Diagnostics

The paper introduces a Composite Certainty Framework to address metric degeneracy in parameter inference by aggregating multiple uncertainty metrics into a single consensus score, revealing parameter-specific identifiability profiles and clinical thresholds.

Likelihood-Free Parameter Inference for Spatiotemporal Stochastic Biological Models using Neural Posterior Estimation

The paper develops a neural posterior estimation framework for parameter inference in spatiotemporal stochastic biological models, demonstrating its effectiveness across multiple cell migration models.

MCMC-CE: A Novel and Efficient Algorithm for Estimating Small Right-Tail Probabilities of Quadratic Forms with Applications in Genomics

The paper introduces MCMC-CE, an algorithm for estimating small right-tail probabilities of quadratic forms, showing improved accuracy and efficiency in genomics applications.

Generation of virtual populations for quantitative systems pharmacology through advanced sampling methods

This paper evaluates DREAM(ZS) for generating virtual populations in QSP, showing it outperforms Metropolis-Hastings in parameter space exploration and correlation preservation.

Trajectory matching-random walk feasibility sampling for simulating heterogeneous dynamics in mechanistic models

This paper introduces TM-RWFS, a method to generate heterogeneity in mechanistic models by constraining trajectories between data bounds, showing it efficiently reproduces biological variability.

ABC for high-dimensional modular models via MCMC samples

The paper introduces an improved ABC-PMC method for high-dimensional modular models by leveraging precomputed MCMC samples and adaptive summary statistics, significantly reducing computational cost.

An automated convergence diagnostic for phylogenetic MCMC analyses

The paper introduces an automated convergence diagnostic for phylogenetic MCMC analyses, demonstrating high accuracy in detecting convergence issues and enabling real-time evaluation.

hopsy - a methods marketplace for convex polytope sampling in Python

hopsy provides a Python platform for accessing Markov chain Monte Carlo sampling algorithms for convex polytopes, enabling collaboration between developers and users.

Missing data in amortized simulation-based neural posterior estimation

This paper addresses missing data in amortized simulation-based neural posterior estimation by proposing methods to encode and handle missing values, showing improved robustness in inference.

Posterior marginalization accelerates Bayesian inference for dynamical systems

This paper introduces a method to accelerate Bayesian inference for dynamical systems by marginalizing the posterior distribution, improving computational efficiency by up to 50 times in effective sample size.

Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation

This paper improves approximate Bayesian computation by combining adaptive scale normalization with regression-based summary statistics and sensitivity weights, enhancing accuracy and efficiency on heterogeneous data.

Optimus: a general purpose adaptive optimisation engine in R

Optimus is an R-based optimisation engine that enables flexible and adaptive parameter sampling for computational biology tasks.

CHRRT: boosting coordinate hit-and-run with rounding by thinning

The paper introduces CHRRT, a modified Coordinate Hit-and-Run with Rounding algorithm that uses thinning to improve computational efficiency for sampling convex polytopes, achieving up to three orders of magnitude faster effective sample size per time.

Efficient inference for agent-based models of real-world phenomena

This paper compares machine learning methods to improve inference efficiency for agent-based simulations of real-world phenomena like tumor growth and disease spread.

Flexible and efficient simulation-based inference for models of decision-making

This paper introduces MNLE, a simulation-based inference method for decision-making models, showing it is more efficient and accurate than existing methods.

Efficient Bayesian inference for mechanistic modelling with high-throughput data

The paper introduces a minibatch Bayesian inference method to efficiently analyze high-throughput imaging data, revealing density-dependent interactions in wound healing.

A flexible Bayesian framework for unbiased estimation of timescales

This paper introduces a Bayesian framework for estimating timescales from dynamic processes, showing it outperforms standard methods on synthetic and primate cortex data.

Algorithmic Parameter Estimation and Uncertainty Quantification for Hodgkin-Huxley Neuron Models

The paper introduces a Bayesian MCMC method with adaptive parallel tempering to estimate parameters and quantify uncertainty in Hodgkin-Huxley neuron models using voltage trace data.

Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

The paper introduces a parallelization strategy to speed up marginal likelihood estimation in Bayesian phylogenetics, reducing runtime significantly with multiple CPUs.

Convergence Assessment for Bayesian Phylogenetic Analysis using MCMC simulation

This paper develops and tests convergence assessment methods for Bayesian phylogenetic analysis using MCMC simulations and implements them in an R package.

Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data

This paper introduces robust distance functions for ABC that handle outliers, showing improved performance on corrupted data while maintaining comparable results on clean data.

An Introduction to Thermodynamic Integration and Application to Dynamic Causal Models

This paper introduces thermodynamic integration for Bayesian model selection and averaging in dynamic causal models, demonstrating its feasibility with efficient implementation and parallel processing.