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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.7K
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...
7.7K
Sampling Distribution01:12

Sampling Distribution

12.2K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

54
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
54
Probability Histograms01:17

Probability Histograms

11.0K
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.
11.0K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

621
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
621

You might also read

Related Articles

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

Sort by
Same author

Genetic variants of p21 and p27 and hepatocellular cancer risk in a Chinese Han population: a case-control study.

International journal of cancer·2012
Same author

Inhibition of TGF-β/Smad signaling by BAMBI blocks differentiation of human mesenchymal stem cells to carcinoma-associated fibroblasts and abolishes their protumor effects.

Stem cells (Dayton, Ohio)·2012
Same author

MAIGO2 is involved in abscisic acid-mediated response to abiotic stresses and Golgi-to-ER retrograde transport.

Physiologia plantarum·2012
Same author

The internal dynamics of mini c TAR DNA probed by electron paramagnetic resonance of nitroxide spin-labels at the lower stem, the loop, and the bulge.

Biochemistry·2012
Same author

Electrochemical depassivation of zero-valent iron for trichloroethene reduction.

Journal of hazardous materials·2012
Same author

Derivation of quantum work equalities using a quantum Feynman-Kac formula.

Physical review. E, Statistical, nonlinear, and soft matter physics·2012
Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: May 24, 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

2.4K

Bayesian Transfer Filtering Using Pseudo Marginal Measurement Likelihood.

Shunyi Zhao, Tianyu Zhang, Yuriy S Shmaliy

    IEEE Transactions on Cybernetics
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Bayesian transfer filter (BTF) that enhances Kalman filtering by integrating unbiased finite impulse response (UFIR) filter knowledge. The BTF improves robustness against noise uncertainties in dynamic systems.

    More Related Videos

    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

    10.0K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    11.8K

    Related Experiment Videos

    Last Updated: May 24, 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

    2.4K
    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

    10.0K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    11.8K

    Area of Science:

    • Signal Processing
    • Control Systems
    • Machine Learning

    Background:

    • Integrating unbiased finite impulse response (UFIR) filters into Kalman filters (KF) presents challenges in knowledge transfer and performance degradation due to noise uncertainties.
    • Existing methods struggle to effectively address noise uncertainties, leading to suboptimal performance in integrated filtering systems.

    Purpose of the Study:

    • To develop a novel Bayesian transfer filter (BTF) that effectively integrates UFIR filter advantages into the KF framework.
    • To enhance robustness against noise uncertainties by refining Bayesian posterior distributions using a knowledge-constrained mechanism.

    Main Methods:

    • The proposed Bayesian transfer filter (BTF) reuses the pseudo marginal measurement likelihood of the UFIR filter as a constraint within the KF.
    • Kullback-Leibler (KL) divergence is employed to minimize discrepancies between proposal and target distributions, optimizing the fusion process.
    • A mean square error-based condition is established to prevent negative transfer, ensuring performance gains.

    Main Results:

    • The BTF effectively refines Bayesian posterior distributions, overcoming limitations of traditional weight-based fusion methods and eliminating the need for error covariance.
    • The proposed method demonstrates superior robustness against noise uncertainties compared to existing approaches.
    • Validation through a moving target tracking example and a quadruple water tank experiment confirms the BTF's effectiveness.

    Conclusions:

    • The Bayesian transfer filter (BTF) offers a significant advancement in integrating UFIR filters with Kalman filters.
    • The knowledge-constrained mechanism and KL divergence optimization provide a robust solution for systems with noise uncertainties.
    • The BTF presents a promising approach for improved state estimation in dynamic systems.