Titlebook: Probability in Electrical Engineering and Computer Science; An Application-Drive Jean Walrand Textbook‘‘‘‘‘‘‘‘ 2021 The Editor(s) (if appli

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,Networks—B,1 proves the results on the spreading of rumors. Section 6.2 presents the theory of continuous-time Markov chains that are used to model queueing networks, among many other applications. That section explains the relationships between continuous-time and related discrete-time Markov chains. Sections

現(xiàn)代

,Digital Link—A,roblem that we study in Sect. 7.1. The main tool is Bayes’ Rule. The key notions are maximum a posteriori and maximum likelihood estimates. Transmission systems use codes to reduce the number of bits they need to transmit. Section 7.2 explains the Huffman codes that minimize the expected number of b

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,Tracking—A,lustrated in Sect. 9.1. The basic problem is defined in Sect. 9.2. One commonly used approach is the linear least squares estimate explained in Sect. 9.3. A related notion is the linear regression covered in Sect. 9.4. Section 9.5 comments on the problem of overfitting. Sections 9.6 and 9.7 explain

調(diào)整

Tracking: B,er of examples. In this chapter, we derive the Kalman filter and explain some of its properties. We also discuss the extended Kalman filter..Section 10.1 explains how to update an estimate as one makes additional observations. Section 10.2 derives the Kalman filter. The properties of the Kalman filt

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Speech Recognition: A,good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that the listeners hear a corrupted version of the speech..Computers are getting much better at speech recog

Cirrhosis

Speech Recognition: B,n. The stochastic gradient projection algorithm is a general technique to update estimates based on additional observations; it is widely used in machine learning. Section 12.2 presents the theory behind that algorithm. When analyzing large amounts of data, one faces the problems of identifying the

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Route Planning: A,ate” of the system. We start with a simple example: choosing a route with uncertain travel times. We then examine a more general model: controlling a Markov chain..Section 13.1 presents a model of route section when the travel times are random. Section 13.2 shows one formulation where one plans the

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