Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

334
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
334
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
226
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
223
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

264
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
264
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

478
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
478
State Space Representation01:27

State Space Representation

502
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
502

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Allergy·2026
Same author

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking.

Environmetrics·2026
Same author

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.

Bayesian analysis·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Assessing spatial disparities: a Bayesian linear regression approach.

Biostatistics (Oxford, England)·2025
Same author

Bayesian Data Sketching for Varying Coefficient Regression Models.

Journal of machine learning research : JMLR·2025
Same journal

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Journal of machine learning research : JMLR·2026
Same journal

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same journal

Unsupervised Tree Boosting for Learning Probability Distributions.

Journal of machine learning research : JMLR·2026
Same journal

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.

Journal of machine learning research : JMLR·2026
Same journal

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes.

Journal of machine learning research : JMLR·2026
Same journal

Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.

Journal of machine learning research : JMLR·2026
See all related articles
  1. Home
  3. Dynamic Bayesian Learning For Spatiotemporal Mechanistic Models.
  1. Home
  3. Dynamic Bayesian Learning For Spatiotemporal Mechanistic Models.

Related Experiment Video

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models.

Sudipto Banerjee1, Xiang Chen1, Ian Frankenburg1

  • 1Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, USA.

Journal of Machine Learning Research : JMLR
|December 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We present a Bayesian approach for learning spatiotemporal models using statistical emulation. This method efficiently trains systems from noisy data by combining mechanistic models with Gaussian process regression, enabling accurate dynamics modeling.

Keywords:
Bayesian meldingBayesian transfer learningGaussian process regressioncomputer modelsmechanistic systemsspatiotemporal analysisstate-space modelsuncertainty quantification

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

562

Related Experiment Videos

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

562

Area of Science:

  • Computational Science
  • Statistical Modeling
  • Machine Learning

Background:

  • Dynamical mechanistic models are crucial for understanding complex systems.
  • Traditional methods for parameter inference can be computationally intensive.
  • Integrating mechanistic knowledge with observed data remains a challenge.

Purpose of the Study:

  • To develop a Bayesian framework for learning spatiotemporal dynamical mechanistic models.
  • To enable efficient interpolation and training of mechanistic systems from noisy data.
  • To provide an analytically tractable inference method for model emulation.

Main Methods:

  • Statistical emulation using Gaussian process regression within hierarchical state-space models.
  • Exact inference with analytically accessible posterior distributions in hierarchical matrix-variate Normal and Wishart models.
  • Dynamic Bayesian transfer learning for large-scale emulation.

    • Main Results:

    • Developed an emulated learner for efficient system interpolation and training.
    • Achieved exact inference, avoiding computationally expensive iterative algorithms.
    • Demonstrated applicability to inverse problems in differential equations and black-box computer models.

    Conclusions:

    • The proposed Bayesian learning approach offers an efficient and analytically tractable method for spatiotemporal dynamical systems.
    • Statistical emulation combined with Gaussian processes provides a powerful tool for integrating mechanistic models with data.
    • The framework facilitates robust inference and broad applicability across scientific domains.