Stochastic Computing Fall 2020 Lecture

We go over course mechanics and begin with an introduction to what is uncertainty and give high level examples of systems and ... In this module we examine properties of expectation, variance, covariance, and correlation. Specifically we look at transformations ... In this module we turn our attention to compact summaries about a distribution's shape rather than depicting it as a table or a ... In this module we continue our discussion of random variable by introducing random vectors. We discuss the joint distribution, ... In this module we examine relationships between the Exponential distribution and the Poisson distribution. We develop and ... In this module we look at the sampling routines available in MATLAB for the Bernoulli, Binomial, Negative Binomial, Geometric, ...

In this module, we turn our attention to continuous distributions. We begin with axioms of probability and how real valued random ... In this module, we turn our attention to the Central Limit Theorem. We begin with explanation of how one can modify the Normal ... In this module, we derive the mean and variance for the Poisson Distribution. We make use of the Maclauren series for e^x to ... In this module we continue with properties of variance and covariance with an alternate method of describing the variability in a ... In this module we focus on formal definitions of a probability. We begin with set operations and how they are used to construct ... In this module we examine the uniform distribution and the exponential distribution. We describe each one and and their mean ...