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Maximum likelihood estimate (mle)
| Glossary of Statistical Terms |
The maximum likelihood estimate of a parameter from data is the possible value of the parameter for which the chance of observing the data largest. that is, suppose that the parameter is p, and that we observe data x. then the maximum likelihood estimate of p is
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Likelihood, английский
- Вероятность. правдоподобие.
- Правдоподобие; вероятность
- In probabilistic and statistical methods, the likelihood is a measure of the evidence for the data when a hypothesis is assumed to be true. it is commonly used in bayesian and quasi-bayesian techniques. a related measure, minimum message length, can be derived from information theoretic principles. as an example, suppose you observe a data value x that is assumed to be gaussian. the likelihood for that data when the assumed mean is, say, 5, and the variance is 10, is proportional to e-((x-5)2/2*10), the kernel of a gaussian distribution. a likelihood is typically calculated using the kernel, rather than the complete distribution function, which includes the normalizing constants. see also: minimum message length.
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Population mean, английский
- The mean of the numbers in a numerical population. for example, the population mean of a box of numbered tickets is the mean of the list comprised of all the numbers on all the tickets. the population mean is a parameter. c.f. sample mean.
- Математическое ожидание; среднее по совокупности pop-up 1. всплывающий элемент; 2. всплывающий pop-up help всплывающая подсказка pop-up list 1. всплывающий список; 2. очередь pop-up menu всплывающее меню; меню, выводимое на экране; меню во временном окне pop-up mouse menu всплывающее меню, управляемое с помощью мыши pop-up note всплывающее примечание pop-up window всплывающее окно; временное рабочее окно 4-port четырехпортовый
Marginal probability distribution, английский
The marginal probability distribution of a random variable that has a joint probability distribution with some other random variables is the probability distribution of that random variable without regard for the values that the other random variables take. the marginal distribution of a discrete random variable x1 that has a joint distribution with other discrete random variables can be found from the joint distribution by summing over all possible values of the other variables. for example, suppose we roll two fair dice independently. let x1 be the number of spots that show on the first die, and let x2 be the total number of spots that show on both dice. then the joint distribution of x1 and x2 is as follows:
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