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- * Let {{M|X\sim\text{Geometrically}(p)}} where {{M|p}} is the [[probability]] of an event happening. ** {{M|1=\mathbb{P}\left[X=n\right]=(1-p)^{n-1}p}} - that is the probability of an event not happing {{M|n-1}} times, then happening on the {{M|n^\text{968 B (171 words) - 19:37, 2 June 2016
- Define a [[probability space]], {{M|(S,\Omega,\mathbb{P})}} as follows: [[Category:Probability]][[Category:Statistics]]7 KB (1,100 words) - 19:36, 13 September 2017
- There are a few distinct cases we may define the uniform distribution on, however in any case the concept is clear: ...the entire {{link|sample space|probability}}, here denoted {{M|S}}, of a [[probability space]] here denoted {{M|(S,\Omega,\mathbb{P})}}1 KB (192 words) - 05:41, 15 January 2018
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- * Don't forget about [[Standard normal distribution]]! [[User:Alec|Alec]] ([[User talk:Alec|talk]]) 01:30, 14 December 2017 (UT The normal distribution has a [[Probability density function]] or [[PDF]], {{M|f:\mathbb{R}\rightarrow\mathbb{R} }} giv1 KB (217 words) - 01:31, 14 December 2017
- * [[Normal (distribution)]] - a [[probability distribution]]245 B (27 words) - 12:19, 14 December 2018
- * Let {{M|X\sim\text{Geometrically}(p)}} where {{M|p}} is the [[probability]] of an event happening. ** {{M|1=\mathbb{P}\left[X=n\right]=(1-p)^{n-1}p}} - that is the probability of an event not happing {{M|n-1}} times, then happening on the {{M|n^\text{968 B (171 words) - 19:37, 2 June 2016
- |title=Poisson distribution |data1=[[Discrete probability distribution|Discrete]], over {{M|\mathbb{N}_{\ge 0} }}8 KB (1,401 words) - 00:52, 20 July 2018
- : ''[[Probability frequency function]]'' and ''[[frequency function (probability)]]'' redirect here. A [[probability density function]] but for {{link|discrete probability distribution||s}}255 B (32 words) - 07:16, 20 September 2017
- #REDIRECT [[Uniform probability distribution]] {{Definition|Statistics|Probability}}167 B (14 words) - 08:26, 20 September 2017
- There are a few distinct cases we may define the uniform distribution on, however in any case the concept is clear: ...the entire {{link|sample space|probability}}, here denoted {{M|S}}, of a [[probability space]] here denoted {{M|(S,\Omega,\mathbb{P})}}1 KB (192 words) - 05:41, 15 January 2018
- : See [[generating samples from a distribution]] for and overview of methods used Let {{M|f(x)}} be a [[probability density function|p.d.f]] for which computing {{M|F^{-1}(x)}} is either diff3 KB (484 words) - 00:18, 1 October 2017
- #* This would model each row as [[Binomial distribution|{{M|\text{Bin} }}]]{{M|\left(\frac{1}{6},45\right)}} #* For example, there were 38 values of six, if random we'd expect the probability of any individual 6 value being recorded in any particular digit as 1/55 KB (670 words) - 15:05, 2 October 2017
- ** [[Geometric distribution]] - a [[probability distribution]] with ties to geometric sequences.3 KB (494 words) - 23:08, 6 October 2017
- |title=Binomial distribution |label1=[[Probability mass function|p.m.f]]4 KB (653 words) - 13:11, 22 September 2017
- ...ith {{M|X_i\sim}}[[Borv|{{M|\text{Borv} }}]]{{M|(p)}}, so {{M|p}} is the [[probability]] of any particular trial being a "success". The geometric distribution models the probability that the ''first'' success occurs on the {{M|k^\text{th} }} trial, for {{M|3 KB (557 words) - 15:14, 16 January 2018
- * [[probability density function]] {{M|f:[a,b]\rightarrow\mathbb{R}_{\ge 0} }} by {{M|f:x\m {{Fundamental probability distributions navbox|show}}1 KB (204 words) - 05:44, 15 January 2018
- Let {{M|X\sim}}[[Poisson distribution|{{M|\text{Poi}(\lambda)}}]] for some {{M|\lambda\in\mathbb{R}_{>0} }} ...} models the number of events ''per unit time'' - although as with Poisson distribution - any continuum will do2 KB (298 words) - 03:16, 6 April 2018
- ...a point? For the continuous case memoryless {{iff}} it's the [[exponential distribution]] - what about the discrete!}}{{ProbMacro}} {{Definition|Statistics|Probability|Elementary Probability}}1 KB (221 words) - 23:02, 11 November 2017
- * the [[probability density function]], {{M|f:\mathbb{R}_{\ge 0}\rightarrow\mathbb{R}_{\ge 0} } * the [[cumulative distribution function]], {{M|F:\mathbb{R}_{\ge 0}\rightarrow[0,1]\subseteq\mathbb{R} }},1 KB (192 words) - 01:27, 16 March 2018
- ...i!}\cdot e^{-r}\frac{r^{k-i} }{(k-i)!} }} - by definition of the [[Poisson distribution]] ...mbda+r)^k}{k!} }} - which the definition of the probability of a [[Poisson distribution|Poisson random variable]] being equal to {{M|k}} whose rate parameter is {{3 KB (536 words) - 22:46, 4 November 2017
- {{ProbMacro}}{{Todo|Link with [[Poisson distribution]] page}} Let {{M|X\sim}}[[Poisson distribution|{{M|\text{Poi} }}]]{{M|(\lambda)}} for some {{M|\lambda\in\mathbb{R}_{>0} }7 KB (1,308 words) - 00:27, 8 November 2017
- ...\lambda}} here is used to denote 2 things}} - the parameter to the Poisson distribution, and the restriction of the 1 dimensional [[Lebesgue measure]] to some regi ...which itself could be viewed as a [[rectangular distribution|rectangular]] distribution's [[random variable]]2 KB (287 words) - 20:59, 26 February 2018
- ...\ \ldots,\ X_n}} be {{i.i.d}} [[random variables]] that are sampled from a distribution, {{M|X}} and additionally let {{M|M:\eq\Max(X_1,\ldots,X_n)}} for short. ...n}} (and use {{M|F(x):\eq \P{X\le x} }}, as is usual for {{link|cumulative distribution function||s}}) {{Caveat|Do not confuse the 's for derivatives}}, then:4 KB (791 words) - 18:57, 26 November 2017
