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

Poisson Probability Distribution01:09

Poisson Probability Distribution

7.7K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

48
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...
48
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
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...
4.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

292
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...
292
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

244
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
244
Probability Histograms01:17

Probability Histograms

11.0K
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.
11.0K

You might also read

Related Articles

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

Sort by
Same author

Model-free machine learning-based 3D single molecule localisation microscopy.

Journal of microscopy·2025
Same author

Immobile lipopolysaccharides and outer membrane proteins differentially segregate in growing <i>Escherichia coli</i>.

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

Design and Synthesis of Covalent Inhibitors of FabA.

ACS omega·2023
Same author

A framework for evaluating the performance of SMLM cluster analysis algorithms.

Nature methods·2023
Same author

A novel and robust method for counting components within bio-molecular complexes using fluorescence microscopy and statistical modelling.

Scientific reports·2022
Same author

Synthesis of a Series of Diaminoindoles.

The Journal of organic chemistry·2021
Same journal

Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.

Statistics and computing·2026
Same journal

Subgroup Analysis of Differential Networks with Latent Variables.

Statistics and computing·2026
Same journal

Non-negative matrix factorization algorithms generally improve topic model fits.

Statistics and computing·2026
Same journal

Approximating evidence via bounded harmonic means.

Statistics and computing·2026
Same journal

Efficient Inference in First Passage Time Models.

Statistics and computing·2026
Same journal

Optimal <i>F</i>-score Matching for Bipartite Record Linkage.

Statistics and computing·2026
See all related articles

Related Experiment Video

Updated: May 15, 2025

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

11.3K

Online Bayesian changepoint detection for network Poisson processes with community structure.

Joshua Corneck1, Edward A K Cohen1, James S Martin1

  • 1Department of Mathematics, Imperial College London, 180 Queen's Gate, London, SW7 2AZ UK.

Statistics and Computing
|April 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an online method to detect changes in network point process structure. The approach accurately identifies shifts in latent node memberships and edge process rates in real-time.

Keywords:
Network point processOnline variational inferenceStochastic block modelStreaming data

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.1K

Related Experiment Videos

Last Updated: May 15, 2025

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

11.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.1K

Area of Science:

  • Statistics
  • Network Analysis
  • Machine Learning

Background:

  • Network point processes often have underlying structures influencing their behavior.
  • These latent structures are not always static, making change detection crucial.
  • Identifying changes in network dynamics is essential for understanding complex systems.

Purpose of the Study:

  • To develop a novel online methodology for detecting changes in the latent structure of network point processes.
  • To address the challenge of dynamic latent structures in network analysis.
  • To provide a scalable and accurate method for real-time network change detection.

Main Methods:

  • Focus on block-homogeneous Poisson processes with latent node memberships.
  • Propose a scalable variational procedure for online analysis.
  • Utilize a Bayesian forgetting factor for sequential variational approximations.
  • Apply the framework to both simulated and real-world network data.

Main Results:

  • The proposed framework rapidly and accurately detects changes in latent edge process rates.
  • It effectively identifies shifts in latent node group memberships in an online manner.
  • Demonstrated effectiveness on simulated data and a real-world bike-sharing network.

Conclusions:

  • The developed online methodology is effective for detecting dynamic changes in network point process structures.
  • The approach accurately captures real-time alterations in network behavior, including those related to external factors.
  • The framework offers a valuable tool for analyzing evolving network phenomena.