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Rating 4.9 stars (70,075) We can do this computation only because we have a simple convolution. The exact is in quotes because we still use slice sampling to estimate the integral of (5.7). The mean of the predictive distribution is 44.91 months with a standard deviation of 30.32 months. Note that maple cannot perform the inversion here because is not an integer, nor can we use partial fraction expansion as described in Section 3.1. The predictive CDF plot is not given; however, the exact and saddlepoint approximated CDFs are in agreement. In Figure 5.20, the shape of the hazard function starts out like a gamma hazard and then approaches the value 0.02468 in the limit. In general, when T is the rst passage time in a semi-Markov process, the asymptotic limit of the hazard approaches the upper bound of the convergence strip of the MGF (i.e., the upper limit of the saddlepoint). This result is presented without proof in Butler and Bronson (2002). In this case, it is the smallest pole of the MGF that is the smallest or in the posterior slice samples [i.e., s < min( i , i ) for all i = 1, . . . , 2000]. Example 5.9: Slice Sampling for Cancer Progression. We return to the general model for cancer progression of Example 5.4 and illustrate the use of slice sampling. We consider two settings: one with the same prior distribution as the rejection sampling of Example 5.4, referred to as SS I; the second, referred to as SS II, has priors that are more vague. The priors for SS II are p Uniform(0, 1) 1 Gamma(1/100, 1/100) 2 Gamma(1/100, 1/100) 1 Uniform(0, 100) 2 Uniform(0, 100). (5.62) (5.63) (5.64) (5.65) (5.66) pdf compressor software: Free PDF Compressor - Download pdf merger software free download for windows 10 7 Best PDF Merge / Combine Software for PC (Offline - Free ...
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