Sebaran probabilitas: Béda antarrépisi

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m Ngarapihkeun éjahan, replaced: oge → ogé , nyaeta → nyaéta, rea → réa (2), ngarupakeun → mangrupa (5), dipake → dipaké (4), energi → énérgi, ea → éa (6), eo → éo (3), kabeh → kabéh using AWB
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Dina [[matematika]], '''sebaran probabilitas''' nangtukeun unggal [[interval (matematika)|interval]] tina [[wilangan nyata]] [[kamungkinan]], mangka kitu [[aksioma probabilitas]] ''terpenuhi''. Dina watesan teknik, probabiliti sebaran nyaetanyaéta [[ukuran probabilitas]] numana domain mangrupa [[aljabar Borel]] dina kaayaan riil.
 
Probabilitas sebaran dina kasus husus ngarupakeunmangrupa notasi nu leuwih tina ukuran probabilitas, nyaéta fungsi nu assigns probabilities satisfying the [[Kolmogorov axioms]] to the measurableméasurable sets of a [[aljabar sigma|measurable space]].
 
Unggal [[variabel acak]] gives rise to a probability distribution, and this distribution contains most of the important information about the variable. If ''X'' is a random variable, the corresponding probability distribution assigns to the interval [''a'', ''b''] the probability Pr[''a'' ≤ ''X'' ≤ ''b''], i.e. the probability that the variable ''X'' will take a value in the interval [''a'', ''b''].
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pikeun ''x'' anggota '''R'''.
 
{{tarjamahkeun|Inggris}}
A distribution is called ''discrete'' if its cumulative distribution function consists of a sequence of finite jumps, which meansméans that it belongs to a [[discrete random variable]] ''X'': a variable which can only attain values from a certain finite or [[countable]] set.
A distribution is called ''continuous'' if its cumulative distribution function is [[continuous]], which meansméans that it belongs to a random variable ''X'' for which Pr[ ''X'' = ''x'' ] = 0 for all ''x'' in '''R'''.
 
The so-called ''absolutely continuous distributions'' can be expressed by a [[fungsi dénsitas probabilitas]]: a non-negative [[Lebesgue integration|Lebesgue integrable]] function ''f'' defined on the realsréals such that
 
:<math>
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== Jejer penting dina sebaran probabiliti ==
 
Sababaraha sebaran probabiliti kacida pentingna dina teoritéori atawa pamakeanpamakéan dibere ngaran nu husus:
* Sebaran diskrit
** Dina kaayaan terhingga
*** [[degenerate distribution|Sebaran ''degenerate'']] dina ''x''<sub>0</sub>, numana ''X'' ngarupakeunmangrupa nilai penting dicokot jadi nilai ''x<sub>0</sub>''. This does not look random, but it satisfies the definition of [[variabel acak]]. This is useful because it puts deterministic variables and random variables in the same formalism.
*** The discrete [[sebaran seragam|uniform distribution]], where all elements of a finite [[set theory|set]] are equally likely. This is supposed to be the distribution of a balanced coin, an unbiased die, a casino roulette or a well-shuffled deck. Also, one can use measurementsméasurements of quantum states to generate uniform random variables. All these are "physical" or "mechanical" devices, subject to design flaws or perturbations, so the uniform distribution is only an approximation of their behaviour. In digital computers, [[Pseudorandom number sequence|pseudo-random number generators]] are used to produced a [[randomness|statistically random]] discrete uniform distribution.
*** The [[Bernoulli distribution]], which takes value 1 with probability ''p'' and value 0 with probability ''q''=1-''p''.
*** [[Sebaran binomial]], nu ngajelaskeun jumlah kasuksesan dina deret tina percobaan bebas Enya/Henteu.
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** With infinite support
*** The [[geometric distribution]], a discrete distribution which describes the number of attempts needed to get the first success in a series of independent Yes/No experiments.
*** The [[negative binomial distribution]], a generalization of the geometricgéometric distribution to the ''n''th success.
*** The [[Poisson distribution]], which describes the number of rare events that happen in a certain time interval.
*** The [[Boltzmann distribution]], a discrete distribution important in [[statistical physics]] which describes the probabilities of the various discrete energy levels of a system in [[thermal equilibrium]]. It has a contiuous analogue. Special cases include
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**** The [[Bose-Einstein distribution]]
**** The [[Fermi-Dirac distribution]]
*** The [[zeta distribution]] has uses in applied statistics and statistical mechanics, and perhaps may be of interest to number theoriststhéorists.
* Continuous distributions
** Supported on a finite interval
*** [[sebaran seragam]] dina [''a'',''b''], numana sakabehsakabéh titik dina interval nu kawengku mibanda ajen nu ampir sarua.
*** [[Sebaran beta]] dina [0,1], ngarupakeunmangrupa sebaran seragam dina kasus husus, nu dipakedipaké dina estimasi probabiliti sukses.
*** The [[Triangular distribution]] on [a, b]
** Supported on semi-infinite intervals, usually [0,∞)
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*** The [[Log-normal distribution]], describing variables which can be modelled as the product of many small independent positive variables.
*** The [[Weibull distribution]], of which the exponential distribution is a special case, is used to model the lifetime of technical devices.
*** [[Sebaran chi-kuadrat]], nu ngarupakeunmangrupa jumlah kuadrat ''n'' variabel random Gauss bebas. Ieu ngarupakeunmangrupa kasus husus dina sebaran Gamma, sarta dipakedipaké dina tes goodness-of-fit dina [[statistik]].
*** [[Sebaran-F]], numana sebaran rasio dua sebaran variabel normal, dipakedipaké dina [[analisa varian]].
** Supported on the whole realréal line
*** [[Sebaran normal]], disebut ogeogé Gaussian atawa kurva bel. It is ubiquitous in nature and statistics due to the [[central limit theorem]]: every variable that can be modelled as a sum of many small independent variables is approximately normal.
*** [[Sebaran-t student]], dipakedipaké keur nga-''estimasi'' meanméan nu teu dipikanyaho dina populasi ''Gaussian''.
*** [[Sebaran Cauchy]], conto sebaran nu teu ngabogaan [[nilai ekspektasi]] atawa [[varian]]. Dina fisika biasana disebut Lorentzian, sarta ieu sebaran tina tetapan energiénérgi teu stabil dina mekanika kuantum. Dina fisika partikel, the extremely short-lived particles associated to such unstable states are called [[resonance]]s.
* Joint distributions
** Two or more random variables on the same sample space