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

Weighted Mean00:57

Weighted Mean

5.6K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.6K
What are Estimates?01:06

What are Estimates?

5.5K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
5.5K
The Availability Heuristic01:08

The Availability Heuristic

6.6K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.6K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K
Sampling Plans01:23

Sampling Plans

310
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
310
Random Sampling Method01:09

Random Sampling Method

12.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
12.7K

You might also read

Related Articles

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

Sort by
Same author

Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.

Journal of imaging informatics in medicine·2024
Same author

Viewpoint Selection for 3D-Games with f-Divergences.

Entropy (Basel, Switzerland)·2024
Same author

Validating hidden Markov models for seabird behavioural inference.

Ecology and evolution·2024
Same author

Dimethyl fumarate-related immune and transcriptional signature is associated with clinical response in multiple sclerosis-treated patients.

Frontiers in immunology·2023
Same author

Robust Multiple Importance Sampling with Tsallis <i>φ</i>-Divergences.

Entropy (Basel, Switzerland)·2022
Same author

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation.

Entropy (Basel, Switzerland)·2022
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 2, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Generalizing the Balance Heuristic Estimator in Multiple Importance Sampling.

Mateu Sbert1, Víctor Elvira2,3

  • 1Informatics and Applications Institute, Girona University, 17003 Girona, Spain.

Entropy (Basel, Switzerland)
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new generalized framework for multiple importance sampling estimators, improving upon the balance heuristic method. The novel approach optimizes parameters to minimize variance, offering better efficiency for approximating complex integrals.

Keywords:
Kullback–Leibler divergenceMonte Carlobalance heuristicchi-square divergencecross entropyimportance samplingvariance reduction

More Related Videos

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

10.0K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Related Experiment Videos

Last Updated: Oct 2, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
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

10.0K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Area of Science:

  • Computational Mathematics
  • Numerical Analysis

Background:

  • Monte Carlo methods are crucial for approximating intractable integrals.
  • The balance heuristic is a widely used but limited Monte Carlo technique.

Purpose of the Study:

  • To propose a novel and generic family of multiple importance sampling (MIS) estimators.
  • To establish a generalized framework for combining samples from multiple proposals.
  • To optimize parameters for variance minimization in MIS estimators.

Main Methods:

  • Developed a generalized framework for MIS by treating sampling rates and combination coefficients as free parameters.
  • Analyzed theoretical variance to determine optimal parameter choices.
  • Conducted numerical experiments using five examples to compare performance.

Main Results:

  • The proposed generalized framework includes the balance heuristic as a special case.
  • Theoretical analysis shows the optimal generalized estimator consistently outperforms the balance heuristic.
  • Numerical examples demonstrate the superior efficiency of the new estimator, especially with an equal number of samples.

Conclusions:

  • The novel generalized MIS framework offers significant improvements over the classical balance heuristic.
  • Optimal parameter selection is key to achieving enhanced estimator performance.
  • New heuristics derived from theoretical findings further enhance practical applicability.