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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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,...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

You might also read

Related Articles

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

Sort by
Same author

A foundation model for continuous glucose monitoring data.

Nature·2026
Same author

Deep phenotyping of health-disease continuum in the Human Phenotype Project.

Nature medicine·2025
Same author

Estimating wage disparities using foundation models.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Causal Representation Learning from Multi-modal Biomedical Observations.

ArXiv·2025
Same author

Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT.

ArXiv·2025
Same author

Interpretable adenylation domain specificity prediction using protein language models.

bioRxiv : the preprint server for biology·2025
Same journal

Classification Under Local Differential Privacy with Model Reversal and Model Averaging.

Journal of machine learning research : JMLR·2026
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
See all related articles

Related Experiment Video

Updated: May 31, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Mixed Membership Stochastic Blockmodels.

Edoardo M Airoldi1, David M Blei, Stephen E Fienberg

  • 1Princeton University ( eairoldi@princeton.edu ).

Journal of Machine Learning Research : JMLR
|June 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new mixed membership stochastic blockmodel for analyzing relational data, like social and protein networks. It offers object-specific representations and uses variational inference for efficient analysis.

More Related Videos

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

Related Experiment Videos

Last Updated: May 31, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

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

Area of Science:

  • Network analysis
  • Statistical modeling
  • Machine learning

Background:

  • Relational data, such as social networks and protein interactions, often violate exchangeability assumptions.
  • Traditional blockmodels struggle with complex relational structures.

Purpose of the Study:

  • To introduce the mixed membership stochastic blockmodel for analyzing relational data.
  • To provide object-specific low-dimensional representations of latent relational structure.
  • To develop an efficient inference algorithm for this model.

Main Methods:

  • Developed a latent variable model: the mixed membership stochastic blockmodel.
  • Implemented a general variational inference algorithm for approximate posterior inference.
  • Applied the model to analyze social and protein interaction networks.

Main Results:

  • The mixed membership stochastic blockmodel effectively captures latent relational structure in complex networks.
  • The variational inference algorithm enables fast and accurate posterior inference.
  • Demonstrated the model's utility in analyzing real-world social and protein interaction data.

Conclusions:

  • The mixed membership stochastic blockmodel is a powerful tool for understanding complex relational data.
  • This approach offers improved object-specific representations compared to traditional methods.
  • The developed inference algorithm facilitates practical application in network analysis.