Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

Probability Distributions

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 in Statistics

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...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Poisson Probability Distribution

Poisson Probability Distribution

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...

You might also read

Related Articles

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

Sort by

Same author

Metastable dynamical computing with energy landscapes: A primer.

Chaos (Woodbury, N.Y.)·2026

Same author

Way More than the Sum of Their Parts: From Statistical to Structural Mixtures.

Entropy (Basel, Switzerland)·2026

Same author

Unsupervised discovery of extreme weather events using universal representations of emergent organization.

Chaos (Woodbury, N.Y.)·2025

Same author

Intrinsic and Measured Information in Separable Quantum Processes.

Entropy (Basel, Switzerland)·2025

Same author

Controlled erasure as a building block for universal thermodynamically robust superconducting computing.

Chaos (Woodbury, N.Y.)·2025

Same author

Enumerating Finitary Processes.

Entropy (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Aug 20, 2025

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

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

Published on: June 27, 2013

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes.

Sarah E Marzen¹, James P Crutchfield²

¹W. M. Keck Science Department of Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA.

Entropy (Basel, Switzerland)

|November 24, 2022

Summary

This summary is machine-generated.

Researchers developed new methods for inferring models and predicting outcomes in continuous-time processes. These novel techniques leverage neural networks for enhanced Bayesian structural inference, improving continuous-time event process analysis.

Keywords:

Poisson process Shannon entropy rate hidden Markov chain hidden semi-Markov process minimal predictor optimal predictor renewal process ϵ-machine

More Related Videos

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

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

Published on: January 23, 2017

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Related Experiment Videos

Last Updated: Aug 20, 2025

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

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

Published on: June 27, 2013

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

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

Published on: January 23, 2017

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Area of Science:

Machine Learning
Statistical Modeling
Information Theory

Background:

Established methods effectively model discrete-time processes.
Continuous-time discrete-event processes are prevalent but less studied.
Existing techniques for continuous-time processes have limitations.

Purpose of the Study:

To introduce novel methods for inferring, predicting, and estimating continuous-time discrete-event processes.
To extend Bayesian structural inference using neural network capabilities.
To provide a more comprehensive framework for analyzing complex event data.

Main Methods:

Extension of Bayesian structural inference.
Integration of neural networks for universal approximation.
Application to discrete-event processes in continuous time.

Main Results:

The proposed methods demonstrate strong performance in inferring models.
New capabilities for prediction in continuous-time processes are achieved.
Entropy rate estimation for these processes is competitive with state-of-the-art.
Experiments on complex synthetic data validate the approach.

Conclusions:

The developed methods offer a powerful new tool for continuous-time discrete-event process analysis.
Neural network integration enhances the capabilities of Bayesian structural inference.
These advancements are significant for fields dealing with continuous-time event data.