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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

185
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...
185
Weighted Mean00:57

Weighted Mean

6.0K
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...
6.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

157
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
157
Entropy02:39

Entropy

33.7K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
33.7K
Entropy01:18

Entropy

3.3K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.3K
Random Error01:04

Random Error

6.1K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Determining the lifetime distribution using fractional moments with maximum entropy.

Heliyon·2024
Same author

Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean.

Entropy (Basel, Switzerland)·2023
Same author

Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean.

Entropy (Basel, Switzerland)·2021
Same author

Risk Neutral Measure Determination from Price Ranges: Single Period Market Models.

Entropy (Basel, Switzerland)·2020

Related Experiment Video

Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.4K

Maximum Entropy Methods for Loss Data Analysis: Aggregation and Disaggregation Problems.

Erika Gomes-Gonçalves1, Henryk Gzyl2, Silvia Mayoral3

  • 1Independent Consultant, 28014 Madrid, Spain.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

The maximum entropy method offers a robust numerical approach for analyzing loss data distributions across finance, insurance, and engineering. This method effectively computes loss distributions and assesses data dependence, even with measurement errors.

Keywords:
credit riskloss data aggregationloss data analysisloss data disaggregationmaximum entropy methodsoperational risksample dependence of loss distributionssample dependence of risk premia

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.2K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.8K

Related Experiment Videos

Last Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.4K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.2K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.8K

Area of Science:

  • Quantitative Finance
  • Actuarial Science
  • Reliability Engineering

Background:

  • Loss data analysis is critical in finance for risk capital computation and in insurance for risk premia determination.
  • Reliability analysis requires understanding damage accumulation and event occurrence distributions.
  • Often, aggregate risk data is available, but the statistical nature of contributing events is unknown.

Purpose of the Study:

  • To illustrate the application of the maximum entropy method for computing loss distributions.
  • To demonstrate how this method addresses challenges in loss data analysis, including measurement errors and interval data.
  • To assess the dependence of loss distributions on empirical data.

Main Methods:

  • Utilizing the maximum entropy method and its extensions for numerical computation of loss distributions.
  • Applying these methods to handle situations with aggregate risk data and unknown contributing event statistics.
  • Incorporating techniques to manage data with measurement errors and interval-based information.

Main Results:

  • The maximum entropy method provides a robust framework for calculating loss distributions.
  • The approach effectively handles complex scenarios, including aggregate data and noisy measurements.
  • Demonstrated the ability to assess the sensitivity of loss distributions to the underlying empirical data.

Conclusions:

  • The maximum entropy method is a powerful tool for loss data analysis in various quantitative fields.
  • Its robustness and extensibility make it suitable for real-world data challenges, including measurement inaccuracies.
  • This method enhances the accurate computation and understanding of loss distributions and associated risks.