Discrete prior distributions
Prior distributions over a discrete quantities.
PointMassPrior(location) PoissonPrior(mean, lower.limit = 0, upper.limit = Inf) DiscreteUniformPrior(lower.limit, upper.limit)
location |
The location of the point mass. |
mean |
The mean of the Poisson distribution. |
lower.limit |
The smallest value within the support of the
distribution. The prior probability for numbers less than
|
upper.limit |
The largest value within the support of the
distribution. The prior probability for numbers greater than
|
Each function returns a prior object whose class is the same as the function name. All of these inherit from "DiscreteUniformPrior" and from "Prior".
The PoissonPrior assumes a potentially truncated Poisson
distribution with the given mean.
Steven L. Scott steve.the.bayesian@gmail.com
## Specify an exact number of trees in a Bart model (see the BoomBart ## package). ntrees <- PointMassPrior(200) ## Uniform prior between 50 and 100 trees, including the endpoints. ntrees <- DiscreteUniformPrior(50, 100) ## Truncated Poisson prior, with a mean of 20, a lower endpoint of 1, ## and an upper endpoint of 50. ntrees <- PoissonPrior(20, 1, 50)
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