Probability and Stochastic Process Identify for the following 13.3 13.4 13.5 13.6 whether the process is discrete time or continuous time, discrete or continuous value Example 13.3Starting on January 1, we measure the noontime temperature (in degrees Celsius)at Newark Airport every day for one year. This experiment generates a sequence,C(1), C(2), ...,C(365), of temperature measurements. With respect to the two kindsof averages of stochastic processes, people make frequent reference to both ensem-ble averages, such as "the average noontime temperature for February 19," and timeaverages, such as the "average noontime temperature for 1986."Example 13.4Consider an experiment in which we record M(t), the mumber of active calls at atelephone switch at time t, at each second over an interval of 15 minutes. One trialof the experiment might yield the sample function m(t, s) shown in Figure 13.2. Eachtime we perform the experiment, we would observe some other function m(t. s). Theexact m(t, s) that we do observe will depend on many random variables including thenumber of calls at the start of the observation period, the arrival times of the new calls,and the duration of each call. An ensemble average is the average number of calls inprogress att= 403 seconds. A time average is the average number of calls in progressduring a specific 15-minute interval.11:12 PM4/21/202 Example 13.5Suppose that at time instants T= 0,1, 2,...,we roll a die and record the outcome NT where1 < NT <6. We then define the randomprocess X(t) such that for T

Name: Probability and Stochastic Process Identify for the following 13.3 13.
Uploaded: 2024-03-20T07:07:50.000Z
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Description: Probability and Stochastic Process Identify for the following 13.3 13.4 13.5 13.6 whether the process is discrete time or continuous time, discrete or continuous value Example 13.3Starting on January 1, we measure the noontime temperature (in degrees