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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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...
Probability Histograms01:17

Probability Histograms

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.
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
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...
Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...

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Related Experiment Video

Updated: Jun 26, 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

A stochastic block prior for clustering in graphical models.

Nikola Sekulovski1, Giuseppe Arena1, Jonas Haslbeck1

  • 1Department of Psychology, University of Amsterdam.

Psychological Methods
|June 25, 2026
PubMed
Summary

This study introduces a new Bayesian graphical model using the stochastic block model to account for clustering in psychological networks. This method helps identify groups of related variables in complex data.

Related Experiment Videos

Last Updated: Jun 26, 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

Area of Science:

  • Psychology
  • Network Science
  • Statistical Modeling

Background:

  • Psychological variables are often conceptualized as complex interacting systems.
  • Graphical models represent these interactions as networks, but current methods often ignore inherent variable clustering.

Purpose of the Study:

  • To address the gap in statistical estimation of psychological networks by incorporating the assumption of clustering.
  • To propose a novel Bayesian graphical modeling framework using the stochastic block model for binary and ordinal data.

Main Methods:

  • Utilized the stochastic block model as a prior distribution for network structure.
  • Embedded the stochastic block model within a Bayesian graphical modeling framework.
  • Applied the method to binary and ordinal psychological data.

Main Results:

  • The proposed method formally incorporates theoretical expectations about variable clustering.
  • Researchers can test hypotheses regarding the number of clusters and estimate node cluster membership.
  • Demonstrated performance through simulation studies and reanalysis of 30 empirical datasets.

Conclusions:

  • The Bayesian graphical model with stochastic block priors offers a robust approach to analyzing clustered psychological networks.
  • This framework enhances the ability to model complex psychological systems by accounting for latent group structures.
  • Facilitates hypothesis testing and estimation of cluster membership in cross-sectional psychological data.