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

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
Associative Learning01:27

Associative Learning

1.5K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.5K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.0K
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...
2.0K
Observational Learning01:12

Observational Learning

1.0K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.0K
Hindsight Biases01:12

Hindsight Biases

4.3K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.3K
Prediction Intervals01:03

Prediction Intervals

3.5K
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. 
3.5K

You might also read

Related Articles

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

Sort by
Same author

Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion.

Entropy (Basel, Switzerland)ยท2023
Same author

Kurtosis-Based Symbol Timing and Carrier Phase/Frequency Tracking.

Entropy (Basel, Switzerland)ยท2021
Same author

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates.

Entropy (Basel, Switzerland)ยท2020
Same author

Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns.

Entropy (Basel, Switzerland)ยท2020
Same author

Exploration vs. Data Refinement via Multiple Mobile Sensors.

Entropy (Basel, Switzerland)ยท2020
Same author

SPARSE BAYESIAN LEARNING USING VARIATIONAL BAYES INFERENCE BASED ON A GREEDY-BASED CRITERION.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2018
Same journal

Deep infant brain segmentation from multi-contrast MRI.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2026
Same journal

Physics-driven Learned Deconvolution of Multi-spectral Cellular MRI with Radial Sampling.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2025
Same journal

Multilevel State-Space Models Enable High Precision Event Related Potential Analysis.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2024
Same journal

A novel method for 12-lead ECG reconstruction.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2024
Same journal

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2023
Same journal

Topological Knowledge Distillation for Wearable Sensor Data.

Conference record. Asilomar Conference on Signals, Systems & Computersยท2023
See all related articles

Related Experiment Video

Updated: Feb 19, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.6K

SPARSE BAYESIAN LEARNING BOOSTED BY PARTIAL ERRONEOUS SUPPORT KNOWLEDGE.

Mohammad Shekaramiz1, Todd K Moon1, Jacob H Gunther1

  • 1ECE Department and Information Dynamics Laboratory, Utah State University.

Conference Record. Asilomar Conference on Signals, Systems & Computers
|November 7, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparse Bayesian learning model to recover sparse signals with unknown clustering. The algorithm corrects erroneous prior knowledge and identifies true signal clustering by filtering incorrect support information.

Keywords:
Sparse Bayesian learning (SBL)clustered patterncompressive sensingerroneous support aidedsingle measurement vector (SMV)

Related Experiment Videos

Last Updated: Feb 19, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.6K

Area of Science:

  • Signal Processing
  • Machine Learning
  • Statistical Inference

Background:

  • Sparse signal recovery is crucial in various fields.
  • Existing methods struggle with unknown clustering and erroneous prior support knowledge.
  • Accurate signal support identification is essential for reliable recovery.

Purpose of the Study:

  • To develop a robust sparse Bayesian learning model for signals with unknown clustering patterns.
  • To incorporate and refine partial, erroneous prior knowledge on signal supports.
  • To simultaneously learn the signal's clustering structure and improve recovery accuracy.

Main Methods:

  • A modified sparse Bayesian learning (SBL) framework is proposed.
  • An additional layer is integrated into the support-aided SBL (SA-SBL) algorithm.
  • Priors are placed on Gamma distribution shape parameters, linked to estimated support variations.

Main Results:

  • The proposed algorithm effectively corrects erroneous prior support information.
  • It successfully learns the underlying clustering pattern of the sparse signal.
  • Simulation results demonstrate improved sparse signal recovery compared to existing methods.

Conclusions:

  • The enhanced SA-SBL model offers a powerful approach for sparse signal recovery with unknown clustering.
  • The method robustly handles and corrects inaccurate prior knowledge on signal supports.
  • This work advances the field of sparse signal processing by enabling simultaneous clustering and recovery.