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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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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...
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Multiple Pipe Systems01:21

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Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
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General Characteristics of Pipe Flow I01:22

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Pipe flow refers to the movement of fluids within fully enclosed conduits, typically cylindrical in shape, such as water pipes or hydraulic hoses. These conduits are designed to withstand high-pressure gradients that drive fluid movement, contrasting with open-channel flows, where gravity is the primary driving force. Rectangular conduits, like air conditioning and heating ducts, generally operate at lower pressures and are less suited for high-pressure applications.
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General Characteristics of Pipe Flow II01:24

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When fluid enters a pipe, it first passes through the entrance region, where the velocity profile adjusts due to viscous effects. In this region, a boundary layer forms along the pipe walls and grows until it fully occupies the pipe's cross-section. Once the boundary layer merges, the flow becomes fully developed, with a steady velocity profile that remains consistent along the pipe's length.
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Single Pipe Systems01:24

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In pipe flow analysis, problems are typically categorized into three types — Type I, Type II, and Type III — based on the known parameters and the desired outcome. Each type of problem addresses specific engineering requirements using fluid properties, pipe characteristics, and operational conditions.
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Related Experiment Video

Updated: Aug 22, 2025

The Diffusion of Passive Tracers in Laminar Shear Flow
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Continuous Probability Distributions generated by the PIPE Algorithm.

Luis G B Pinho1, Juvêncio S Nobre1, Gauss M Cordeiro2

  • 1Universidade Federal do Ceará (UFC), Centro de Ciências, Campus do Pici, Av. Mister Hull, s/n, Bloco 910, 60440-900 Fortaleza, CE, Brazil.

Anais Da Academia Brasileira De Ciencias
|November 9, 2022
PubMed
Summary
This summary is machine-generated.

The Probabilistic Incremental Programming Evolution (PIPE) algorithm effectively models data distributions by evolving probability rules. This method offers greater control over function complexity and simultaneously handles model selection and estimation.

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Area of Science:

  • Computational Statistics
  • Machine Learning
  • Data Modeling

Background:

  • Traditional methods for modeling data distributions often involve parameterizing existing probability distributions.
  • Model selection and parameter estimation are typically separate, sequential processes.
  • Controlling the complexity of derived models can be challenging with existing techniques.

Purpose of the Study:

  • To investigate the Probabilistic Incremental Programming Evolution (PIPE) algorithm for constructing continuous cumulative distribution functions.
  • To evaluate PIPE's capability in fitting empirical data distributions.
  • To compare PIPE's performance against existing distribution modeling approaches.

Main Methods:

  • Utilizing the PIPE algorithm to generate candidate functions based on evolving probability rules.
  • Applying optimality criteria to iteratively refine candidate functions.
  • Controlling model complexity by defining allowed mathematical functions and expression length.
  • Simultaneously performing model selection and parameter estimation.

Main Results:

  • The PIPE algorithm successfully generated candidate functions to fit empirical data distributions.
  • PIPE demonstrated superior performance on both simulated and real-world datasets.
  • For real data, PIPE achieved higher data likelihoods than existing models.
  • PIPE-generated models were found to be mathematically simpler than those from conventional methods.

Conclusions:

  • The PIPE algorithm provides a robust and flexible framework for continuous cumulative distribution function construction.
  • PIPE offers advantages in controlling model complexity and integrating model selection with estimation.
  • This approach yields competitive or superior results compared to traditional methods, with enhanced model parsimony.