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

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

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...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

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...

Estimation of the Physical Quantities

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

Estimating Population Mean with Unknown Standard Deviation

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...

Estimating Population Standard Deviation

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

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...

You might also read

Related Articles

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

Sort by

Same author

Higher-order neuromorphic Ising machines-autoencoders and Fowler-Nordheim annealers are all you need for scalability.

Nature communications·2026

Same author

Sequential analysis and its applications to neuromorphic engineering.

Frontiers in neuroscience·2026

Same author

Event-Based Vision Sensor Lifetime Degradation in Low Earth Orbit.

Sensors (Basel, Switzerland)·2025

Same author

A network-based analysis to identify a piRNA-target signature related to colorectal cancer prognosis: in silico and in vitro study.

Discover oncology·2025

Same author

Letter to the Editor Regarding "Modified Frailty Index Questionnaire as a Predictor of Discharge Destination in Elderly Patients Undergoing Multilevel Lumbar Interbody Fusion".

World neurosurgery·2025

Same author

The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes.

Neural computation·2025

Same journal

CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain.

Frontiers in neuroscience·2026

Same journal

Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

Frontiers in neuroscience·2026

Same journal

Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair.

Frontiers in neuroscience·2026

Same journal

Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Frontiers in neuroscience·2026

Same journal

Sex-divergent intrinsic brain function in Parkinson's disease: elevated nigral fluctuations and premotor-visuospatial coupling in female patients.

Frontiers in neuroscience·2026

Same journal

Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.

Frontiers in neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 23, 2026

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Bayesian Estimation and Inference Using Stochastic Electronics.

Chetan Singh Thakur¹, Saeed Afshar¹, Runchun M Wang¹

¹Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.

Frontiers in Neuroscience

|April 6, 2016

Summary

This summary is machine-generated.

This study implements hardware for real-time Bayesian inference using stochastic circuits. Two systems, BEAST and BIND, demonstrate probabilistic algorithms for tracking and Directed Acyclic Graph inference, showcasing noise robustness.

Keywords:

Bayesian inference Hidden Markov models Sequential Monte Carlo sampling direct acyclic graph neuromorphic engineering probabilistic graphical models spiking neural networks stochastic computation

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Related Experiment Videos

Last Updated: Mar 23, 2026

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Area of Science:

Hardware implementation of probabilistic algorithms
Real-time Bayesian inference
Stochastic computing circuits

Background:

Bayesian inference problems are computationally intensive.
Emerging integrated circuit technologies face noise challenges.
Need for robust hardware solutions for probabilistic computations.

Purpose of the Study:

Demonstrate real-time Bayesian inference in hardware.
Implement probabilistic algorithms using a unified set of building blocks.
Showcase noise robustness of stochastic electronic implementations.

Main Methods:

Implemented Bayesian Estimation and Stochastic Tracker (BEAST) for Hidden Markov Model (HMM) based target tracking.
Developed Bayesian INference in DAG (BIND) using stochastic circuits for Directed Acyclic Graph (DAG) inference.
Utilized simple digital logic gates for building block implementation.

Main Results:

BEAST successfully tracked a target in real-time, learning transition and observation models, and external noise probabilities.
BIND demonstrated inference in DAGs using stochastic circuits.
Stochastic electronic implementation showed robustness to noise, crucial for nanometer-scale ICs.

Conclusions:

Hardware implementation of Bayesian inference is feasible using stochastic circuits.
The proposed building blocks enable real-time probabilistic computations.
Stochastic computing offers a noise-resilient approach for future integrated circuits.