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

Factorial Design02:01

Factorial Design

15.5K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
15.5K
Improper Integrals: Infinite Intervals01:29

Improper Integrals: Infinite Intervals

246
An integral is classified as improper due to an infinite interval when at least one of its limits of integration extends to positive or negative infinity. In such cases, the region under the curve is unbounded, and standard techniques for evaluating definite integrals are not directly applicable. Instead, the improper integral is defined through a limiting process that allows one to determine whether the accumulated area remains finite despite the infinite domain.Application to Exponential...
246
Binomial Probability Distribution01:15

Binomial Probability Distribution

16.6K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.6K
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.4K
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...
12.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

482
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
482
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K

You might also read

Related Articles

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

Sort by
Same author

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification.

International journal of computer vision·2025
Same author

Probabilistic neural transfer function estimation with Bayesian system identification.

PLoS computational biology·2024
Same author

Mathematical discoveries from program search with large language models.

Nature·2023
Same author

Signal domain adaptation network for limited-view optoacoustic tomography.

Medical image analysis·2023
Same author

A benchmark dataset for machine learning in ecotoxicology.

Scientific data·2023
Same author

Regularizing transformers with deep probabilistic layers.

Neural networks : the official journal of the International Neural Network Society·2023

Related Experiment Video

Updated: Mar 30, 2026

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.7K

Infinite Factorial Unbounded-State Hidden Markov Model.

Isabel Valera, Francisco J R Ruiz, Fernando Perez-Cruz

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 17, 2015
    PubMed
    Summary

    This study introduces an infinite factorial unbounded-state hidden Markov model (IFUHMM) for complex temporal sequences. The model effectively recovers independent causes when the number of causes and states are unknown, demonstrated in power disaggregation.

    More Related Videos

    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

    11.8K

    Related Experiment Videos

    Last Updated: Mar 30, 2026

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
    11:22

    Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

    Published on: January 30, 2018

    10.7K
    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

    11.8K

    Area of Science:

    • Artificial Intelligence
    • Signal Processing
    • Machine Learning
    • Statistical Modeling

    Background:

    • Many AI, signal processing, and medical scenarios involve temporal sequences with unknown, overlapping independent causes.
    • Factorial Hidden Markov Models (FHMMs) are suitable for these scenarios but often require pre-defined numbers of causes and states.
    • Limitations exist when the number of causes or states in FHMMs cannot be predetermined.

    Purpose of the Study:

    • To propose an Infinite Factorial Unbounded-State Hidden Markov Model (IFUHMM) capable of handling an unknown number of parallel Hidden Markov Models (HMMs) and an unbounded number of states.
    • To extend existing infinite factorial HMMs to accommodate an arbitrary number of states.
    • To develop an inference algorithm for the proposed model that balances the complexity of unbounded states and chains.

    Main Methods:

    • Development of a Bayesian Nonparametric (BNP) prior over integer-valued matrices to model the states of multiple Markov chains over time.
    • Extension of the infinite factorial binary-state HMM to support an arbitrary number of states.
    • Modification of the model to allow for an unbounded number of states.
    • Derivation of a Markov Chain Monte Carlo (MCMC)-based inference algorithm to handle the model's unbounded nature.

    Main Results:

    • The proposed IFUHMM successfully models temporal sequences with an unknown number of independent causes and an unbounded number of states.
    • The developed MCMC inference algorithm effectively manages the trade-offs inherent in models with unbounded states and chains.
    • The model's performance was validated using the power disaggregation problem, demonstrating its practical applicability.

    Conclusions:

    • The IFUHMM offers a flexible and powerful framework for causal discovery in complex temporal data where model complexity is initially unknown.
    • The Bayesian nonparametric approach and MCMC inference provide a robust solution for scenarios previously intractable with standard FHMMs.
    • The proposed model shows significant promise for applications requiring the recovery of latent causal structures from observed time series data.