Search results
Results from the WOW.Com Content Network
The gamma distribution is the conjugate prior for the precision of the normal distribution with known mean. The matrix gamma distribution and the Wishart distribution are multivariate generalizations of the gamma distribution (samples are positive-definite matrices rather than positive real numbers).
The Gamma distribution, which describes the time until 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 parameter, developed to predict waiting times in queuing systems; The inverse-gamma distribution; The generalized gamma distribution
The Gamma distribution is parameterized by two hyperparameters ... is the sample mean; mean was estimated from observations with total precision ...
In probability theory and statistics, the normal-gamma distribution (or Gaussian-gamma distribution) is a bivariate four-parameter family of continuous probability distributions. It is the conjugate prior of a normal distribution with unknown mean and precision .
The gamma distribution is an exponential family with two parameters, ... is the sample mean of the observations. Background and interpretation ...
Once the sample mean is known, no further information about μ can be obtained from the sample itself. On the other hand, for an arbitrary distribution the median is not sufficient for the mean: even if the median of the sample is known, knowing the sample itself would provide further information about the population mean. For example, if the ...
In Bayesian statistics, the Wishart distribution is a conjugate prior for the precision parameter of the multivariate normal distribution, when the mean parameter is known. [11] A generalization is the multivariate gamma distribution.
The marginal distribution of a gamma process at time is a gamma distribution with mean / and variance /. That is, the probability distribution f {\displaystyle f} of the random variable X t {\displaystyle X_{t}} is given by the density f ( x ; t , γ , λ ) = λ γ t Γ ( γ t ) x γ t − 1 e − λ x . {\displaystyle f(x;t,\gamma ,\lambda ...