Directory

Gaussian distribution, with its bell-shaped curve, depicts symmetric continuous data, commonly used for modeling normally distributed data. In contrast, the Poisson distribution, a discrete distribution, captures the occurrence of rare events within fixed intervals, making it ideal for modeling situations where event rates are consistent. Both distributions are defined by their mean (μ), representing the…

Poisson equation, a fundamental partial differential equation, models the distribution of a scalar function in a volume given its source term. It finds applications in diverse fields such as electrostatics, fluid dynamics, and heat transfer. Numerical methods like finite difference method aid in solving Poisson equation, with tools like COMSOL and MATLAB facilitating computational analysis….

The maximum likelihood estimator (MLE) for the binomial distribution is a statistical method used to estimate the probability of success in a binomial experiment. It is based on the assumption that the observed data is the most likely outcome given the true probability of success. The likelihood function is a function that measures the probability…

Maximum likelihood estimation (MLE) for the Poisson distribution involves finding the value of the mean parameter (lambda) that maximizes the likelihood function. This function is based on the probability mass function of the Poisson distribution and the observed count data. The log-likelihood function and its derivative (the score function) are used to determine the maximum…

MLE (Maximum Likelihood Estimation) for the Poisson distribution determines the most probable value of its mean parameter based on observed count data. Using the likelihood function derived from the Poisson distribution’s probability mass function, the MLE estimator is obtained by finding the value of the mean that maximizes the likelihood. This estimator is widely used…

The moment generating function (MGF) of the gamma distribution, denoted as (M_X(t)), is given by (M_X(t) = \left(1 – \frac{t}{\beta}\right)^{-\alpha}), where (t) is the variable, (\alpha) is the shape parameter, and (\beta) is the rate parameter. The MGF captures the complete probabilistic information about the random variable following the gamma distribution. It enables the derivation…

The zero-inflated Poisson (ZIP) distribution is a statistical model that accounts for the overabundance of zero observations and the Poisson distribution of non-zero counts. It combines a mixture distribution and a Poisson distribution, with a probability parameter for the excess of zeros. The ZIP model addresses data situations with a high proportion of zero counts,…

The gamma-Poisson distribution is a probability distribution that models the distribution of the number of events that occur in a given time interval or space, where the rate of events is itself a random variable following a gamma distribution. It is useful in situations where the intensity of events varies over time or space, and…

The gamma distribution in R is a continuous probability distribution characterized by its shape and rate parameters. Key functions for this distribution include dgamma() for probability density, pgamma() for cumulative distribution, qgamma() for quantile, and rgamma() for random sampling. Its mean and variance can be calculated based on the given parameters. The gamma distribution relates…

The Poisson-binomial distribution, a hybrid of the binomial and Poisson distributions, arises when the number of trials is random and follows a Poisson distribution. It’s used when the probability of success (p) is small and the number of trials (n) is large, allowing for efficient approximation of the binomial distribution. This distribution finds applications in…

The moment generating function of a random variable provides essential information about its distribution. For the gamma distribution, it is given by , where is the shape parameter and is the rate parameter. This function enables the calculation of moments, such as the mean and variance, and facilitates the investigation of distribution properties. It plays…

Maximum likelihood estimation (MLE) for the gamma distribution involves finding the values of the distribution’s parameters that maximize the likelihood function. The likelihood function is the probability of observing the sample data, given the parameters. By taking the logarithm of the likelihood function and solving for the parameters that maximize it, we can obtain the…