**Probability and Stochastic (Random) Processes (optional):**

Conditional expectation, Poisson process, Markov chains, renewal theory, queueing theory, reliability theory, Brownian motion and stationary processes. This topic is particularly useful for electrical and computer engineers, actuarial study, finance or things like that. See also left material from stage 2 probability. If you want to learn this with measure, see stage 4 probability list.

- Baclawski K. and Rota G.-C.
*An Introduction to Probability and Random Processes*[.pdf] (FREE!) - de Finetti B.
*Theory of Probability: A Critical Introductory Treatment*- The author is notable for "operational subjective". - Feller W.
*An Introduction to Probability Theory and Its Applications Vol. 1*- "A colorful and rich introduction to probability theory and its applications" according to Karlin and Taylor, but it "is limited in that it deals only with discrete probabilities." - Gray R.M. and Davisson L.D.
*Introduction to Statistical Signal Processing*(FREE!) - Grimmett G.R. and Stirzaker D.R.
*Probability and Random Processes* - Hsu H.
*Schaum's Outline of Probability, Random Variables, and Random Processes* - Kao E.P.C.
*An Introduction to Stochastic Processes* - Karlin S. and Taylor H.M.
*A First Course in Stochastic Processes* - Krishnan V.
*Probability and Random Processes* - Lebanon G. Tutorial Notes (FREE!) - Read R1 - R5.
- Mörters P. Lecture Notes on Probability Theory [.ps] (FREE!)
- Papoulis A.
*Probability, Random Variables and Stochastic Processes*- See their web for resources. - Ross S.M.
*Introduction to Probability Models*- Note this is an introduction to probability and stochastic processes, not an introduction to basic probability theory. This text could be quite hard to follow without knowing very basic probability theory. Also note that, no graph has been used when the author introduce Markov chains for the first time. - Walrand J. Lecture Notes on Probability Theory and Random Processes [.pdf] (FREE!)
- Yates R.D. and Goodman D.J.
*Probability and Stochastic Processes: An Friendly Introduction for Electrical and Computer Engineers*

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