Summability of Double Independent Random Variables

Summability of Double Independent Random Variables

We will examine double sequence to double sequence transformation of independent identically distribution random variables with respect to four-dimensional summability matrix methods. The main goal of this paper is the presentation of the following theorem. If maxk,l|am,n,k,l| maxk,l|am,kan,l| O m−γ1 O n−γ2 , γ1, γ2 > 0, then E|X̆|1 1/γ1 < ∞ and E| ̆̆ X|1 1/γ2 < ∞ imply that Ym,n → μ almost sure P...

On Nörlünd Summability of Random Variables to Zero

Pairwise Independent Random Variables

In this lecture we discuss how to derandomize algorithms. We will see a brute force algorithm (enumeration) for derandomization. We will also see that some random algorithms do not need true randomness. Specifically, we will see an example where only pairwise random bits are needed. Next, we will see how we can generate pairwise random values and how this conservation on the amount of randomnes...

Independent random variables

1 Last two lectures ¯ probability spaces ¯ probability measure ¯ random variables and stochastic processes ¯ distribution functions ¯ independence ¯ conditional probability ¯ memoriless property of geometric and exponential distributions ¯ expectation ¯ conditional expectation (double expectation) ¯ mean-square estimation 1

Conditionally independent random variables

In this paper we investigate the notion of conditional independence and prove several information inequalities for conditionally independent random variables. Keywords— Conditionally independent random variables, common information, rate region.