A probability density function (PDF) describes the likelihood of different outcomes for a continuous random variable.

Fundamental methods are developed for the derivation of the probability density function and moments of rational algebraic functions of independent random variables. Laplace and Mellin integral ...

Provides a one-semester course in probability and statistics with applications in the engineering sciences. Probability of events, discrete and continuous random variables cumulative distribution, ...

Explain why probability is important to statistics and data science. See the relationship between conditional and independent events in a statistical experiment. Calculate the expectation and variance ...

Probability, statistics, reliability and decision with applications in engineering. Probability of events, discrete and continuous random variables, probability density functions and distributions, ...

This is a preview. Log in through your library . Abstract The notion of probability density for a random function is not as straight-forward as in finite-dimensional cases. While a probability density ...

Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model.

We will cover the axioms of probability, counting formulas, independence and conditional probability, discrete and continuous random variables, jointly distributed random variables, expectations, ...

Introduction to probability theory and its applications. Axioms of probability, distributions, discrete and continuous random variables, conditional and joint distributions, correlation, limit laws, ...