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

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

347
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
347
Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K
Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

726
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
726
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
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...
333
Probability Histograms01:17

Probability Histograms

8.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
8.7K

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

A zero-inflated hierarchical generalized transformation model to address non-normality in spatially-informed cell-type deconvolution.

Biometrics·2026
Same author

A Zero-Inflated Hierarchical Generalized Transformation Model to Address Non-Normality in Spatially-Informed Cell-Type Deconvolution.

bioRxiv : the preprint server for biology·2026
Same author

Assessing spatial disparities: a Bayesian linear regression approach.

Biostatistics (Oxford, England)·2025
Same author

Investigating the Aliso Canyon gas blowout disaster and adverse birth outcomes: A quasiexperimental approach.

Science advances·2025
Same journal

A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures (with Discussion).

Bayesian analysis·2026
Same journal

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.

Bayesian analysis·2026
Same journal

A Two-Component <i>G</i>-Prior for Variable Selection.

Bayesian analysis·2026
Same journal

Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals.

Bayesian analysis·2026
Same journal

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.

Bayesian analysis·2025
Same journal

Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity.

Bayesian analysis·2025
See all related articles
  1. Home
  3. Bayesian Inference For Spatial-temporal Non-gaussian Data Using Predictive Stacking.
  1. Home
  3. Bayesian Inference For Spatial-temporal Non-gaussian Data Using Predictive Stacking.

Related Experiment Video

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K

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

Soumyakanti Pan1, Lu Zhang2, Jonathan R Bradley3

  • 1Department of Biostatistics, University of California Los Angeles.

Bayesian Analysis
|April 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Bayesian inference using predictive stacking for non-Gaussian spatial-temporal data. This method improves computational efficiency and convergence for complex ecological models.

Keywords:
Primary 62exponential family datamodel averagingsecondary 62Aspatial-temporal generalised linear models

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Related Experiment Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Area of Science:

  • Statistics
  • Computational Biology
  • Ecological Modeling

Background:

  • Analyzing non-Gaussian spatial-temporal data presents challenges in generalized linear models due to difficulties in analytically integrating random effects.
  • Standard inference methods struggle with convergence for weakly identified parameters in these models.

Purpose of the Study:

  • To develop a novel Bayesian inference method for non-Gaussian spatial-temporal data.
  • To improve computational efficiency and overcome convergence issues in complex statistical models.
  • To apply the method to real-world ecological data, specifically avian count data.

Main Methods:

  • Devised Bayesian inference using predictive stacking to assimilate information from analytically tractable conditional posterior distributions.
  • Expanded on Diaconis-Ylvisaker conjugate priors and utilized generalized conjugate multivariate (GCM) distribution theory for exponential families.
  • Enabled exact sampling from posterior distributions conditional on process parameters and assimilated inference over parameter ranges.

    • Main Results:

    • The proposed method demonstrates effective inferential performance on simulated data.
    • Comparison with full Bayesian inference using Markov chain Monte Carlo (MCMC) shows comparable results.
    • Successfully applied to analyze spatially-temporally referenced avian count data from the North American Breeding Bird Survey.

    Conclusions:

    • Bayesian inference with predictive stacking offers a viable and efficient approach for non-Gaussian spatial-temporal data analysis.
    • The method addresses limitations of traditional inference, particularly convergence issues.
    • Provides a robust framework for analyzing complex ecological datasets with spatial and temporal dependencies.