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Probability Distributions01:32

Probability Distributions

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 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...
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Continuous functions exhibit smooth, uninterrupted behavior, and combining them through standard operations retains this continuity. If f and g are continuous at a point a, then the functions f+g, f-g, cf (where c is a constant), fg, and fg (provided g(a)a) are also continuous at a. This allows the construction of complex functions from simpler continuous parts without losing smoothness.Polynomials, which are expressions formed by sums of powers of x with constant coefficients, are continuous...
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Continuous Charge Distributions01:17

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Imagine a bucket of water. It contains many molecules, of the order of 1026 molecules. Thus, although it contains discrete elements (molecules) at the microscopic level, macroscopically, it can be considered continuous. Small volume elements of water, infinitesimal compared to the bulk of the bucket's volume, still contain many molecules. Under this framework, quantized matter is approximated as continuous for practical purposes.
The electric charge can also be subjected to an analogical...
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Normal Distribution01:11

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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
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Continuity of a Function01:23

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A function is continuous at a point a if three conditions are met: the function is defined at a, the limit of the function as x approaches a exists, and this limit equals the function’s value. Mathematically, this is written asThis definition ensures the graph of the function does not exhibit any breaks, holes, or jumps at that point. Discontinuities occur when any of these conditions fail. A removable discontinuity exists when the two-sided limit exists but the function is either...
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Limits with Oscillating Discontinuities01:19

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An oscillating discontinuity is a type of discontinuity in which a function’s values fluctuate infinitely often as the input approaches a particular point. Unlike jump discontinuities, where the function suddenly shifts between two values, or infinite discontinuities, where the function diverges without bound, an oscillating discontinuity arises from rapid back-and-forth variation. Because the function never stabilizes toward a single value, no finite limit exists at that point.One of the...
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Related Experiment Video

Updated: Nov 27, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Majorization and Dynamics of Continuous Distributions.

Ignacio S Gomez1, Bruno G da Costa2, Maike A F Dos Santos3

  • 1Instituto de Física, Universidade Federal da Bahia, Rua Barao de Jeremoabo, Salvador-BA 40170-115, Brazil.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces majorization in continuous distributions to characterize mixing, diffusive, and quantum dynamics. Stationary states are identified as the infimum elements in these majorized ordered chains.

Keywords:
H-theoremcontinuous majorizationconvex functionsordered chain

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Area of Science:

  • Statistical Mechanics
  • Information Theory
  • Quantum Dynamics

Background:

  • Majorization is a concept from information theory used to compare probability distributions.
  • Understanding the evolution of dynamical systems is crucial in various scientific fields.

Purpose of the Study:

  • To demonstrate how majorization in continuous distributions can characterize mixing, diffusive, and quantum dynamics.
  • To connect majorization with the H-Boltzmann theorem.

Main Methods:

  • Utilizing the definition of majorization with a range of convex functions (ϕ).
  • Analyzing time evolution of dynamical systems as majorized ordered chains.
  • Deriving the H-Boltzmann theorem as a special case.

Main Results:

  • Mixing, generalized Fokker-Planck, and quantum dynamics are characterized as majorized ordered chains.
  • Stationary states are identified as the infimum elements in these chains.
  • The H-Boltzmann theorem is obtained for the specific convex function ϕ(x) = x ln x.

Conclusions:

  • Majorization provides a unified framework for characterizing diverse dynamical systems.
  • The choice of convex functions allows for a flexible study of dynamics.
  • This approach offers new insights into the behavior of systems approaching equilibrium.