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

Mixtures of Acids03:27

Mixtures of Acids

21.6K
The pH of a solution containing an acid can be determined using its acid dissociation constant and its initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending upon the relative strength of the acids and their dissociation constants.
A Mixture of a Strong Acid and a Weak Acid
In a mixture of a strong acid and a weak acid, the strong acid dissociates completely and becomes a source of almost all the hydronium ions...
21.6K
Mixtures of Acids01:19

Mixtures of Acids

1.1K
The pH of a solution containing an acid can be determined using its acid dissociation constant and initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending on the relative strength of the acids and their dissociation constants.
In a strong and weak acid mixture, the strong acid dissociates completely and becomes a source of almost all the hydronium ions present in the solution. In contrast, the weak acid shows...
1.1K
Steps in the Modeling Process01:14

Steps in the Modeling Process

653
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
653
Racemic Mixtures and the Resolution of Enantiomers02:30

Racemic Mixtures and the Resolution of Enantiomers

21.6K
A racemic mixture, or racemate, is an equimolar mixture of enantiomers of a molecule that can be separated using their unique interaction with chiral molecules or media. Racemic mixtures are denoted by the (±)- prefix. This ‘optical rotation descriptor’ applies to the whole solution of a racemic mixture rather than a specific stereoisomer. Enantiomers typically have the same physical and chemical properties. Hence, they are not easily separable. However, enantiomers can exhibit...
21.6K
Mixtures of Gases: Dalton's Law of Partial Pressures and Mole Fractions03:03

Mixtures of Gases: Dalton's Law of Partial Pressures and Mole Fractions

43.7K
Unless individual gases chemically react with each other, the individual gases in a mixture of gases do not affect each other’s pressure. Each gas in a mixture exerts the same pressure that it would exert if it were present alone in the container. The pressure exerted by each individual gas in a mixture is called its partial pressure.
43.7K
Processes of Self-Presentation01:29

Processes of Self-Presentation

212
Effective self-presentation is a central component of social interaction and identity construction. It relies on the dynamic processes of defining the situation and engaging in self-disclosure. These mechanisms help individuals navigate social context expectations and manage how others perceive them, fostering mutual understanding and relationship development.Defining the SituationSocial situations are shaped by collectively understood frames—a set of widely understood rules or...
212

You might also read

Related Articles

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

Sort by
Same author

An Optimization Randomized Clinical Trial to Identify an Effective, Efficient Smoking Cessation Intervention in the Context of Lung Cancer Screening: Cessation and Screening to Save Lives (CASTL).

Chest·2026
Same author

Training that sticks: sustaining knowledge and communication excellence in geriatric oncology through a multidisciplinary training program.

Innovation in aging·2026
Same author

The impact of breast cancer polygenic risk score disclosure on decisional conflict around risk-reducing mastectomy in women with pathogenic BRCA1/2 variants.

Genetics in medicine : official journal of the American College of Medical Genetics·2026
Same author

Feasibility, acceptability, and preliminary efficacy of an LGBTQ+ inclusive communication skills training for multidisciplinary oncology healthcare providers.

PEC innovation·2026
Same author

Advancing depression assessment in older adults with cancer: Development and validation of the Older Adults with Cancer-Depression Scale (OAC-D), a novel, patient-reported outcome.

Cancer·2026
Same author

Novel bayesian nonparametric unsupervised learning approach to precision symptom management in cancer survivors: a re-analysis of a comparative effectiveness trial.

Journal of behavioral medicine·2026

Related Experiment Video

Updated: Jan 23, 2026

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.9K

A tutorial on Dirichlet Process mixture modeling.

Yuelin Li1,2, Elizabeth Schofield1, Mithat Gönen2

  • 1Department of Psychiatry & Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY 10022, USA.

Journal of Mathematical Psychology
|June 21, 2019
PubMed
Summary

This tutorial demystifies Bayesian nonparametric (BNP) models for psychology researchers. It clarifies complex concepts and provides a practical guide to using Dirichlet Process Mixture Models (DPMMs) for cognitive analysis.

Keywords:
Bayesian NonparametricChinese Restaurant ProcessDirichlet ProcessGibbs SamplingMixture Model

More Related Videos

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.1K
Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications
06:24

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Jan 23, 2026

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.9K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.1K
Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications
06:24

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications

Published on: January 5, 2024

1.3K

Area of Science:

  • Cognitive Science
  • Computational Statistics
  • Psychological Research Methods

Background:

  • Bayesian nonparametric (BNP) models are increasingly vital in psychology for understanding cognition and data analysis.
  • Existing BNP tutorials often lack accessibility for non-technical audiences.
  • A gap exists in bridging theoretical BNP concepts with practical computational implementation.

Purpose of the Study:

  • To provide an accessible introduction to key Bayesian nonparametric concepts for beginners in psychology.
  • To elucidate the mathematical derivations underlying BNP models, linking theory to practice.
  • To facilitate the application of BNP methods in psychological research and inspire novel methodological development.

Main Methods:

  • Detailed explanation of omitted mathematical derivations for core BNP concepts.
  • Step-by-step walkthrough of a practical computation solution for Dirichlet Process Mixture Models (DPMMs).
  • Line-by-line explanation of a publicly accessible R program for BNP model computation.

Main Results:

  • Abstract BNP concepts are made concrete through explicit theoretical explanations.
  • The tutorial connects BNP algorithms to the Chinese Restaurant Process for intuitive understanding.
  • Readers gain a clear pathway to implementing and adapting BNP methods.

Conclusions:

  • This tutorial enhances understanding of BNP theory and application for psychologists.
  • It empowers researchers to utilize DPMMs and other BNP methods in their work.
  • The provided computational guidance supports both current application and future innovation in psychological modeling.