Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Random Variables

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...

Randomized Experiments

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...

Probability Histograms

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.

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

Emotional words evoke region- and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex.

Cell reports·2026

Same author

A cross-sectional protocol for experimental tongue high-density surface electromyography to detect and classify radiation-associated hypoglossal neuropathy.

PloS one·2026

Same author

A transcriptional program associated with neurotransmission in the living human brain.

Molecular psychiatry·2026

Same author

Shiny-MAGEC: A Bayesian R shiny application for meta-analysis of censored adverse events.

Research synthesis methods·2026

Same author

Electrodiffusion in cardiac intercalated disc nanostructures alters cell-cell action potential transmission via ephaptic coupling: A model study.

The Journal of physiology·2025

Same author

BISON: bi-clustering of spatial omics data with feature selection.

Bioinformatics (Oxford, England)·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

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

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

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 21, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.

Andrea Cremaschi^1,2, Raffaele Argiento^3,4, Katherine Shoemaker^5,6

¹Department of Cancer Immunology, Institute of Cancer Research, Oslo University Hospital, Oslo, Norway.

Bayesian Analysis

|May 21, 2020

Summary

This summary is machine-generated.

This study introduces a new method for analyzing complex data networks that deviate from normal distributions. The approach uses nonparametric hierarchical models for more accurate graphical model inference, especially for heavy-tailed data.

Keywords:

Bayesian nonparametrics graphical models hierarchical models normalized completely random measures radiomics data t-distribution

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Dec 21, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Statistics
Machine Learning
Data Science

Background:

Gaussian graphical models are standard for multivariate normal data.
Deviations from normality, like heavy tails, necessitate alternative distributions.
The Dirichlet t-distribution clusters divisors using a Dirichlet process for contaminated data.

Purpose of the Study:

To generalize Dirichlet t-distributions using normalized completely random measures (NormCRMs).
To propose a nonparametric hierarchical structure for modeling dependence among divisors.
To enhance clustering effectiveness and enable parsimonious parameter sharing across samples.

Main Methods:

Utilizing normalized completely random measures (NormCRMs) as priors for divisor terms.
Implementing a nonparametric hierarchical structure to model dependence among divisors.
Applying the method to analyze dependence structures in radiomics data.

Main Results:

The proposed approach yields accurate graphical model inference.
Simulations demonstrate the effectiveness of the method for non-Gaussian data.
The method successfully applied to radiomics data from The Cancer Imaging Atlas.

Conclusions:

The generalized Dirichlet t-distribution with NormCRMs and hierarchical structures improves network analysis for non-Gaussian data.
This flexible modeling approach allows for accurate clustering and parameter sharing.
The method offers a powerful tool for exploring complex dependence structures in real-world datasets.