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

Probability in Statistics01:14

Probability in Statistics

18.6K
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...
18.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

729
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
729
Probability Distributions01:32

Probability Distributions

10.0K
 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...
10.0K
Randomized Experiments01:13

Randomized Experiments

6.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.3K
Probability Histograms01:17

Probability Histograms

8.7K
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.
8.7K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Human and Deep Learning Predictions of Peripheral Lung Cancer Using a 1.3 mm Video Endoscopic Probe.

Respirology (Carlton, Vic.)·2025
Same author

Decision trees: from efficient prediction to responsible AI.

Frontiers in artificial intelligence·2023
Same author

The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications.

IEEE computer graphics and applications·2023
Same author

Predicting User Preferences of Dimensionality Reduction Embedding Quality.

IEEE transactions on visualization and computer graphics·2022
Same author

Fast Multiscale Neighbor Embedding.

IEEE transactions on neural networks and learning systems·2020
Same author

Dynamics of the perception and EEG signals triggered by tonic warm and cool stimulation.

PloS one·2020

Related Experiment Video

Updated: May 5, 2026

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

2.1K

Pointwise probability reinforcements for robust statistical inference.

Benoît Frénay1, Michel Verleysen1

  • 1Machine Learning Group - ICTEAM, Université catholique de Louvain, Place du Levant 3, 1348 Louvain-la-Neuve, Belgium.

Neural Networks : the Official Journal of the International Neural Network Society
|December 5, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces pointwise probability reinforcements (PPRs) to improve statistical inference with small datasets affected by abnormally frequent data (AFDs). PPRs help mitigate bias caused by outliers, making machine learning models more robust.

Keywords:
CleansingFilteringMaximum likelihoodOutliersProbability reinforcementsRobust inference

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K

Related Experiment Videos

Last Updated: May 5, 2026

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

2.1K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K

Area of Science:

  • Machine Learning
  • Statistical Inference
  • Data Science

Background:

  • Small datasets in machine learning can suffer from abnormally frequent data (AFDs), such as outliers.
  • AFDs can bias parameter estimates in statistical inference, leading to unreliable models.
  • Existing methods struggle to effectively handle these data anomalies.

Purpose of the Study:

  • To introduce a novel method, pointwise probability reinforcements (PPRs), to address the challenge of AFDs in statistical inference.
  • To develop a generic and robust approach applicable to various machine learning tasks.
  • To enable manual filtering of outliers by providing an abnormality degree for each observation.

Main Methods:

  • Pointwise probability reinforcements (PPRs) are proposed to adjust the probability of individual observations.
  • A regularization parameter controls the extent of reinforcement, compensating for AFDs.
  • The method is designed to be compatible with any statistical inference formulated as likelihood maximization.

Main Results:

  • Experiments demonstrate the effectiveness of PPRs in regression, classification, and projection tasks.
  • Models are freed from the undue influence of outliers, leading to more accurate inference.
  • The method provides an abnormality degree for each observation, facilitating manual outlier identification.

Conclusions:

  • Pointwise probability reinforcements (PPRs) offer a generic and effective solution for robust statistical inference in the presence of abnormally frequent data.
  • This approach enhances the reliability of machine learning models, particularly with small or outlier-prone datasets.
  • PPRs enable improved model performance and provide a mechanism for outlier detection and potential removal.