Priors for model parameters

Specify a prior distribution for any estimated parameter. The result is a prior_class object that can be passed to any parameter slot in poisson_likelihood(), nb_likelihood(), the delay process constructors, or the epidemic process constructors.

Usage

std_normal_prior()

normal_prior(mean, sd)

cauchy_prior(location, scale)

student_t_prior(df, location, scale)

double_exponential_prior(mean, sd)

flat_prior()

positive_flat_prior()

half_std_normal_prior()

half_normal_prior(location, scale)

half_cauchy_prior(location, scale)

half_student_t_prior(df, scale)

half_double_exponential_prior(mean, sd)

gamma_prior(shape, rate)

weibull_prior(shape, scale)

inv_gamma_prior(shape, scale)

lognormal_prior(meanlog, sdlog)

chi_square_prior(df)

exponential_prior(rate)

logistic_prior(location, scale)

beta_prior(alpha, beta)

Arguments

mean, location

Location parameter.

sd, scale

Scale parameter (must be > 0).

df

Degrees of freedom (must be > 0).

shape

Shape parameter (must be > 0).

rate

Rate parameter (must be > 0).

meanlog

Mean on the log scale (lognormal).

sdlog

SD on the log scale (lognormal, must be > 0).

alpha, beta

Beta distribution shape parameters (must be > 0).

Value

A prior_class object.

Examples

normal_prior(0, 1)
#> Normal(0, 1)
gamma_prior(2, 0.1)
#> Gamma(2.0, 0.1)
exponential_prior(1)
#> Exponential(1)
flat_prior()
#> Flat()