This article was originally published at The Conversation. The publication contributed the article to Space.com's Expert Voices: Op-Ed & Insights I was preparing for my early morning class back in ...

(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.) Toshi Hirabayashi, Georgia Institute of Technology (THE CONVERSATION) I was preparing ...

Apply arithmetic mean of frequency distribution to find the expected value of a random variable The expected value of discrete random variable as summation of product of discrete random variable by ...

A continuous random variable is a type of variable that can take on any value within a given range. Unlike discrete random variables, which have a countable number of outcomes, continuous random ...

The binomial distribution is a key concept in probability that models situations where you repeat the same experiment several times, and each time there are only two possible outcomes—success or ...

The inverse transform can create a time-domain waveform where no waveform has been before. In part 2 of this series, we used the discrete Fourier transform to convert a waveform from the time domain ...

The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm. A ...

*Refers to the latest 2 years of stltoday.com stories. Cancel anytime. Unsplash Buying real estate is an exciting journey, but you need to be responsible about the money involved. Perhaps one of the ...

Abstract: Consider a system comprising sensors that communicate with a remote estimator by way of a so-called collision channel. Each sensor observes a discrete random variable and must decide whether ...

We consider a discrete-time risk process driven by proportional reinsurance and an interest rate process. We assume that the interest rate process behaves as a Markov chain. To reduce the risk of ruin ...

Basic probability; Discrete random variables, examples (e.g. Bernoulli, binomial, geometric), expected values, variances; Markov chains and their properties ...