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

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.
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Identifying demographic predictors of increased non-fatal opioid overdose risk among New York State Medicaid enrollees following the COVID-19 pandemic: an analysis of heterogeneous treatment effects.

Epidemiology (Cambridge, Mass.)·2026
Same author

Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape.

Frontiers in big data·2025
Same author

Assessing User Engagement With an Interactive Mapping Dashboard for Overdose Prevention Informed by Predictive Modeling in Rhode Island.

Journal of public health management and practice : JPHMP·2025
Same author

Stemming the Tide of the US Overdose Crisis: How Can We Leverage the Power of Data Science and Artificial Intelligence?

The Milbank quarterly·2025
Same author

Investigating heterogeneous effects of an expanded methadone access policy with opioid treatment program retention: a Rhode Island population-based retrospective cohort study.

American journal of epidemiology·2025
Same author

Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.

Preventive medicine·2025

Related Experiment Video

Updated: Jun 4, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Fast Bayesian scan statistics for multivariate event detection and visualization.

Daniel B Neill1

  • 1Carnegie Mellon University, Pittsburgh, PA 15213, USA. neill@cs.cmu.edu

Statistics in Medicine
|February 12, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced multivariate Bayesian scan statistic (MBSS) for detecting irregularly shaped clusters in space-time data. The new method significantly improves detection accuracy and timeliness while maintaining computational efficiency.

More Related Videos

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Related Experiment Videos

Last Updated: Jun 4, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Area of Science:

  • Statistics
  • Data Science
  • Epidemiology

Background:

  • The multivariate Bayesian scan statistic (MBSS) is a framework for detecting events in multivariate space-time data.
  • Existing MBSS methods primarily focus on detecting circular or regularly shaped clusters.
  • There is a need for methods that can detect irregularly shaped clusters in complex data.

Purpose of the Study:

  • To extend the MBSS framework for the detection and visualization of irregularly shaped clusters in multivariate data.
  • To develop an efficient computational method for analyzing all possible subsets of locations.
  • To compare the performance of the new method against the original MBSS approach.

Main Methods:

  • Developed a hierarchical prior over all subsets of locations to model irregular cluster shapes.
  • Implemented the 'Fast Subset Sums' algorithm for efficient computation of posterior probabilities.
  • Compared the 'Fast Subset Sums' method with the original MBSS (circular regions) using semi-synthetic outbreak data.

Main Results:

  • The 'Fast Subset Sums' method enables efficient detection and visualization of irregular clusters.
  • Demonstrated substantial improvements in spatial accuracy and timeliness of detection compared to the original MBSS.
  • Maintained the scalability and fast run time of the original MBSS framework.

Conclusions:

  • The extended MBSS framework effectively detects irregularly shaped clusters in multivariate space-time data.
  • The 'Fast Subset Sums' method offers a computationally efficient solution for complex cluster detection.
  • This advancement enhances the utility of MBSS for real-world event detection and analysis.