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Chapter_02+--+Random+Variables

# Chapter_02+--+Random+Variables - region Figure 3.4(p 108...

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The following graphic shows: a discrete random variable X (its possible values) its PMF (associated likelihood for X takes a specific value), and a derived new random variable Y through a transformation Y = g ( X ) Note: The use of equivalent events or sets in the following calculations P{Y=10} = P{X=1} = 0.15 due to the fact { Y=10} Ù {X=1} P{Y=50} = P{X=6}+P{X=7}+P{X=8} = 0.3 due to {Y=50} Ù {X=6, or X=7, or X=8}

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Figure 2.2 (p. 90) The PMF of Y and the relative frequencies found in two sample runs of voltpower (100). Note that in each run the relative frequencies are close to (but not exactly equal to) the corresponding PMF.

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Figure 3.2 (p. 106) The graph of an arbitrary CDF Fx ( x ). Note: From CDF, one can read out Probabilities of the r.v. falls into a specific

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Unformatted text preview: region. Figure 3.4 (p. 108) Connection btw the PDF and the CDF of X . Figure 3.5 (p. 119) Two examples of a Gaussian random variable X with expected value μ and standard deviation σ . Figure 3.6 (p. 121): Symmetry properties of Gaussian(0,1) PDF. Table 3.1 (p. 123): The standard normal PDF Φ ( y ). Table 3.2 (p. 124): The standard normal complementary CDF Q ( z ). Figure 3.7 (p. 126): As ε → 0, d ε ( x ) approaches the delta function δ ( x ). For each ε , the area under the curve of d ε ( x ) equals 1. Figure 3.9 (p. 138): The b-bit uniform quantizer shown for b = 3 bits. Table 3.3 (p. 143): MATLAB functions for continuous random variables....
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Chapter_02+--+Random+Variables - region Figure 3.4(p 108...

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