Dina matematika, sebaran probabilitas nangtukeun unggal interval tina wilangan nyata kamungkinan, mangka kitu aksioma probabilitas terpenuhi. Dina watesan téhnik, probabiliti sebaran nyaéta ukuran probabilitas nu mana domain mangrupa aljabar Borel dina kaayaan riil.
Probabilitas sebaran dina kasus husus mangrupa notasi nu leuwih tina ukuran probabilitas, nyaéta fungsi nu assigns probabilities satisfying the Kolmogorov axioms to the méasurable sets of a 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].
Sebaran probabilitas variabel X bisa sacara unik didadarkeun ku fungsi sebaran kumulatif F(x), nu ditangtukeun ku
pikeun x anggota R.
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A distribution is called discrete if its cumulative distribution function consists of a sequence of finite jumps, which méans 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 méans that it belongs to a random variable X for which Pr[ X = x ] = 0 for all x in R.
for all a and b. That discrete distributions do not admit such a density is unsurprising, but there are continuous distributions like the devil's staircase that also do not admit a density.
The support of a distribution is the smallest closed set whose complement has probability zero.
Jejer penting dina sebaran probabilitiÉdit
Sababaraha sebaran probabiliti kacida pentingna dina téori atawa pamakéan dibéré ngaran nu husus:
- Sebaran diskrit
- Dina kaayaan terhingga
- Sebaran degenerate dina x0, nu mana X mangrupa nilai penting dicokot jadi nilai x0. 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 uniform distribution, where all elements of a finite 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 méasurements 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, pseudo-random number generators are used to produced a 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 bébas Enya/Henteu.
- The hypergeometric distribution, which describes the number of successes in the first m of a series of n independent Yes/No experiments, if the total number of successes is known.
- 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 géometric distribution to the nth 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
- The zeta distribution has uses in applied statistics and statistical mechanics, and perhaps may be of interest to number théorists.
- Dina kaayaan terhingga
- Continuous distributions
- Supported on a finite interval
- Supported on semi-infinite intervals, usually [0,∞)
- Sebaran eksponensial, which describes the time between consecutive rare random events in a process with no memory.
- Sebaran gamma, nu ngajelaskeun waktu salila n consecutive rare random events occur in a process with no memory.
- The Erlang distribution, which is a special case of the gamma distribution with integral shape paraméter, developed to predict waiting times in queuing systems.
- The Log-normal distribution, describing variables which can be modélled as the product of many small independent positive variables.
- The Weibull distribution, of which the exponential distribution is a special case, is used to modél the lifetime of technical devices.
- Sebaran chi-kuadrat, nu mangrupa jumlah kuadrat n variabel random Gauss bébas. Ieu mangrupa kasus husus dina sebaran Gamma, sarta dipaké dina tes goodness-of-fit dina statistik.
- Sebaran-F, nu mana sebaran rasio dua sebaran variabel normal, dipaké dina analisa varian.
- Supported on the whole réal line
- Sebaran normal, disebut ogé Gaussian atawa kurva bel. It is ubiquitous in nature and statistics due to the central limit theorem: every variable that can be modélled as a sum of many small independent variables is approximately normal.
- Sebaran-t student, dipaké keur nga-estimasi méan nu teu dipikanyaho dina populasi Gaussian.
- Sebaran Cauchy, conto sebaran nu teu mibanda nilai ekspektasi atawa varian. Dina fisika biasana disebut Lorentzian, sarta ieu sebaran tina tetapan énérgi teu stabil dina mekanika kuantum. Dina fisika partikel, the extremely short-lived particles associated to such unstable states are called resonances.
- Joint distributions
- Sebaran nilai-matrix