The section Univariate Distributions (Table 61.7 through Table 61.35) lists all univariate distributions that PROC MCMC recognizes. The section Multivariate Distributions (Table 61.36 through Table 61.40) lists all multivariate distributions that PROC MCMC recognizes. With the exception of the multinomial distribution, all these distributions can be used in the MODEL , PRIOR , and HYPERPRIOR statements. The multinomial distribution is supported only in the MODEL statement. The RANDOM statement supports a limited number of distributions; see Table 61.4 for the complete list.
See the section Using Density Functions in the Programming Statements for information about how to use distributions in the programming statements. To specify an arbitrary distribution, you can use the GENERAL and DGENERAL functions. See the section Specifying a New Distribution for more details. See the section Truncation and Censoring for tips about how to work with truncated distributions and censoring data.
Table 61.7: Beta Distribution
Table 61.10: Cauchy Distribution
PROC specification |
|
Density |
|
Parameter restriction |
|
Range |
|
Mean |
Does not exist. |
Variance |
Does not exist. |
Mode |
a |
Random number |
Generate ; let . Repeat the procedure until . is a draw from the standard Cauchy, and (Ripley, 1987). |
Table 61.13: Exponential Exponential Distribution
PROC specification |
expexpon( |
|
Density |
|
|
Parameter restriction |
|
|
Range |
|
Same |
Mode |
|
|
Random number |
Generate , and is a draw from the exponential exponential distribution. Note that an exponential exponential distribution is not the same as the double exponential distribution. |
|
Relationship to the exponential distribution |
|
Table 61.19: Gamma Distribution
PROC specification |
gamma(a, |
|
Density |
|
|
Parameter restriction |
|
|
Range |
if otherwise. |
Same |
Mean |
ab |
|
Variance |
|
|
Mode |
if |
if |
Random number |
See (McGrath and Irving, 1973). |
Table 61.20: Geometric Distribution
PROC specification |
|
Density * |
|
Parameter restriction |
|
Range |
|
Mean |
round() |
Variance |
|
Mode |
0 |
Random number |
Based on samples obtained from a Bernoulli distribution with probability p until the first success. |
*The random variable is the total number of failures in an experiment before the first success. This density function is not to be confused with another popular formulation, , which counts the total number of trials until the first success. |
Table 61.26: Negative Binomial Distribution
PROC specification |
|
Density |
|
Parameter restriction |
|
Range |
|
Mean |
round |
Variance |
|
Mode |
|
Random number |
Generate , and (Fishman, 1996). |
Table 61.34: Wald Distribution
wald(, ) |
|
Density |
|
Parameter restriction |
|
Range |
|
Mean |
|
Variance |
|
Mode |
|
Random number |
Generate . Let and . Perform a Bernoulli trial, . If , choose ; otherwise, choose (Michael, Schucany, and Haas, 1976). |