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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

194
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,...
194
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

845
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
845
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

562
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
562
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
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...
121
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100

You might also read

Related Articles

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

Sort by
Same author

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same author

Scalable and robust regression models for continuous proportional data.

Journal of the American Statistical Association·2026
Same author

Local graph estimation with pathwise false discovery control.

Nature communications·2026
Same author

Bayesian Transfer Learning.

Statistical science : a review journal of the Institute of Mathematical Statistics·2026
Same author

Domain Adaptive Bootstrap Aggregating.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same authorSame journal

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

Bayesian analysis·2026
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

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

Bayesian analysis·2026
Same journal

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

Bayesian analysis·2026
Same journal

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

Bayesian analysis·2025
See all related articles

Related Experiment Video

Updated: Sep 1, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Latent Nested Nonparametric Priors (with Discussion).

Federico Camerlenghi1,2, David B Dunson3, Antonio Lijoi4

  • 1Department of Economics, Management and Statistics, University of Milano - Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano, Italy.

Bayesian Analysis
|August 18, 2022
PubMed
Summary
This summary is machine-generated.

We introduce novel latent nested processes to model complex dependencies in discrete random structures, overcoming limitations of existing methods for Bayesian nonparametrics. This offers improved flexibility for applications like clustering and topic modeling.

Keywords:
62F1562G05Bayesian nonparametricsPrimary 60G57completely random measuresdependent nonparametric priorsheterogeneitymixture modelsnested processes

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K

Related Experiment Videos

Last Updated: Sep 1, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K

Area of Science:

  • Statistics
  • Bayesian Nonparametrics
  • Machine Learning

Background:

  • Discrete random structures are crucial in Bayesian nonparametrics, with applications in density estimation, clustering, topic modeling, and prediction.
  • Nested processes model dependencies, but existing methods like the nested Dirichlet process can degenerate, limiting their flexibility.

Purpose of the Study:

  • To introduce a novel class of latent nested processes to address limitations in modeling dependence structures.
  • To provide a flexible framework for Bayesian nonparametrics that avoids degeneracy issues.

Main Methods:

  • Developing latent nested processes by combining common and group-specific completely random measures.
  • Normalizing these measures to obtain dependent random probability measures.
  • Deriving results on induced partition distributions and developing a Markov Chain Monte Carlo sampler for Bayesian inference.

Main Results:

  • The proposed latent nested processes effectively model a range of dependence structures, from homogeneity to heterogeneity.
  • A byproduct is a test for distributional homogeneity across groups.
  • The methods are demonstrated on synthetic and real data, showcasing their inferential capabilities.

Conclusions:

  • Latent nested processes offer a powerful and flexible alternative to existing nested processes in Bayesian nonparametrics.
  • These processes enhance modeling capabilities for complex data structures and provide a useful tool for group comparisons.