Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit
Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g., numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceManagementNumerical IntegrationSchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking
Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled rand…
Multi-Object TrackingObject TrackingProjected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow. We prove that a Metropolis-Hastings implementa…
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
This work introduces a new method called scalable Bayesian Monte Carlo (SBMC). The model interpolates between a point estimator and the posterior, and the algorithm is a parallel implementation of a consistent (asymptoti…
Uncertainty QuantificationEnsemble preconditioning for Markov chain Monte Carlo simulation
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multi…
Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories
This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poisson multi-Bernoulli mixture (TPMBM) fil…
Multi-Object TrackingObject Tracking