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in a variation on example 910 we use the observation vector y ynnbsp y1nbspmiddotmiddotmiddot yn to estimate x x1 the
a random sample of 44 automobiles registered in dallas texas show the ages of the cars to be -nbspage in
continuing example 910 the 21-dimensional vector x has correlation matrix rx with i jth elementwe use the observation
in the cdma multiuser communications system introduced in problem 839 each user i transmits an independent data bit xi
when x and y have expected values microxnbsp microynbsp 0 theorem 94 says thatnbsplnbspy nbspnbspshow that this result
x is a 3-dimensional random vector with ex 0 and autocorrelation matrix rxnbspwith elementsy is a 2-dimensional random
for a certain coin q is a uniform 0 1 random variable given q q each flip is heads with probability q independent of
suppose that in quiz 93 r measured in meters has a uniform pdf over 0 1000 find the map estimate of r given x x in
for random variables x and y we wish to use y to estimate x however our estimate must be of the formnbspnbsp aya find
for random variables x and y from problem 914 findnbsply the linear minimum mean square error estimator of x given
here are four different joint pmfsa for each pair of random variables indicate whether the two random variables are
a telemetry voltage v transmitted from a position sensor on a ships rudder is a random variable withnbspa receiver in
the following table gives pxynbspx y the joint probability mass function of random variables x and ya find the marginal
x and y have the joint pdfa what is fx xb what is the blind estimatenbspbc what is the minimum mean square error
generalizing the solution of example 92 let the call duration t be an exponential lambda random variable for t0nbspgt 0
suppose the brownian motion process is constrained by barriers that is we wish to generate a process yt such that -b
in this problem we employ the result of problem 1064 as the basis for a function snewarrivalslambdat that generates a
recall that for a rate lambda poisson process the expected number of arrivals in 0 t is lambdat inspection of the code
a particular telephone switch handles only automated junk voicemail calls that arrive as a poisson process of rate
for the telephone switch of example 1028 we can estimate the expected number of calls in the system emt after t minutes
write a matlabprogram that generates and graphs the noisy cosine sample paths xcct xdct xcd t and xdd t of figure 103
let xt be a gaussian process with mean microxnbspt and autocovariance cxttau in this problem we verify that the for two
xt is a wide sense stationary stochastic process with autocorrelation function rxnbsptau 10 sin2pi1000t2pi1000t the
xt and yt are independent wide sense stationary processes with expected values microx and microy and autocorrelation
let xnnbspbe a wide sense stationary random sequence with expected value microxnbspand autocovariance cxnbspk for m 0