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Related Concept Videos

Binomial Probability Distribution01:15

Binomial Probability Distribution

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,...
Probability Distributions01:32

Probability Distributions

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 probability...
Poisson Probability Distribution01:09

Poisson Probability Distribution

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...
Probability in Statistics01:14

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...
Probability Histograms01:17

Probability Histograms

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.
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

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Related Experiment Video

Updated: Jun 27, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

ODE-BPA: A Novel Basic Probability Assignment Generation Method Based on OTSU and Deng Entropy.

Xinghua Zhou1, Luyuan Chen1, Enze Mao1

  • 1College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces ODE-BPA, a novel method for generating basic probability assignments (BPAs) in Dempster-Shafer theory. ODE-BPA enhances uncertainty reduction and demonstrates competitive performance against existing methods and classifiers.

Keywords:
Dempster–Shafer theoryDeng entropyOTSUbasic probability assignmentsclassification

Related Experiment Videos

Last Updated: Jun 27, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Artificial Intelligence
  • Information Theory
  • Mathematical Foundations

Background:

  • Constructing basic probability assignments (BPAs) objectively and adaptively is crucial in Dempster-Shafer theory.
  • Existing methods face challenges with uncertainty due to rigid nested focal element structures.

Purpose of the Study:

  • Propose ODE-BPA, a new BPA generation method.
  • Reduce uncertainty by addressing rigid focal element structures.
  • Improve adaptive and objective BPA construction.

Main Methods:

  • ODE-BPA normalizes and sorts hypothesis support values into an ordered sequence.
  • Utilizes OTSU for constructing non-overlapping focal elements.
  • Employs Deng entropy to regulate mass allocation confidence.

Main Results:

  • ODE-BPA achieves competitive accuracy and ranking against existing BPA generation methods.
  • Demonstrates performance comparable to SVM, naive Bayes, and decision tree classifiers.
  • Shows robustness by alleviating the influence of local noise and conflicting evidence.

Conclusions:

  • ODE-BPA offers an effective approach for objective and adaptive BPA construction.
  • The method shows promise in handling noisy and conflicting evidence.
  • Contributes to advancing Dempster-Shafer theory applications.