Topic 7: Random Processes
(e.g. for prediction) { Long-term averages { Extreme or boundary events (e.g. outage) { Estimation/detection of a signal corrupted by noise ES150 { Harvard SEAS 3 Two ways of viewing a random process Consider a process X(t;!) † At a flxed t, X(t;!) is …
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The autocorrelation function and the rate of change
www.ece.tufts.eduWhite noise † Band-limited white noise: A zero-mean WSS process N(t) which has the psd as a constant N0 2 within ¡W • f • W and zero elsewhere. { Similar to white light containing all frequencies in equal amounts.
Lecture 1: Entropy and mutual information
www.ece.tufts.edu4.1 Non-negativity of mutual information In this section we will show that I(X;Y) ≥ 0, (27) and this is true for both the discrete and continuous cases. Before we get to the proof, we have to introduce some preliminary concepts like Jensen’s in-equality and the relative entropy.
Topic 6: Convergence and Limit Theorems
www.ece.tufts.eduTopic 6: Convergence and Limit Theorems ... – This is the Central Limit Theorem (CLT) and is widely used in EE. ES150 – Harvard SEAS 7 • Examples: 1. Suppose that cell-phone call durations are iid RVs with μ = 8 and σ = 2 (minutes). – Estimate the probability of 100 calls taking over 840 minutes.
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Game Theory, Alive
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