Difference between revisions of "Statistics notes - on random variables, distributions, probability"

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m (A few distributions)
m (Poisson distribution)
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independently of time since last even.
 
independently of time since last even.
  
Used mainly if the events are relatively sparse; at higher rates it tends towards rh
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Used mainly if the events are discrete, and relatively sparse,
 +
where the distribution sits against zero and noticeably isn't very symmetric.
 +
 
 +
Note that when not so sparse, the shape much resembles a normal distribution.
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In part just because of the CLT, though, and it may still make more sense to model it as Poisson for a few usually-minor reasons (normal is continuous).
  
  
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https://en.wikipedia.org/wiki/Poisson_distribution
 
https://en.wikipedia.org/wiki/Poisson_distribution
 
-->
 
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===Gaussian/Normal distribution===
 
===Gaussian/Normal distribution===

Revision as of 19:03, 4 July 2020

This is more for overview of my own than for teaching or exercise.

Overview of the areas

Arithmetic · 'elementary mathematics' and similar concepts
Set theory, Category theory
Geometry and its relatives · Topology
Elementary algebra - Linear algebra - Abstract algebra
Calculus and analysis
Logic
Semi-sorted
 : Information theory · Number theory · Decision theory, game theory · Recreational mathematics · Dynamical systems · Unsorted or hard to sort


Math on data:

  • Statistics as a field
some introduction · areas of statistics
types of data · on random variables, distributions
Virtues and shortcomings of...
on sampling · probability
glossary · references, unsorted
Footnotes on various analyses

Other data analysis, data summarization, learning

Regression · Classification, clustering, decisions · dimensionality reduction · Optimization theory, control theory
Connectionism, neural nets · Evolutionary computing



This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Dependent versus independent

Random variables, distributions

Probability function(/distribution)

Probability mass function (discrete)

Probability density functions (continuous)

Cumulative distribution function

Expected value

A few distributions

Binomial distribution

Poisson distribution

Gaussian/Normal distribution