Uniform distribution

Usage

create_normal_prior(
  names = NULL,
  mean = 0,
  sd = 1,
  lower = -Inf,
  upper = Inf,
  repair = c("unique", "universal", "check_unique", "unique_quiet", "universal_quiet")
)

create_uniform_prior(
  names = NULL,
  lower = 0,
  upper = 1,
  repair = c("unique", "universal", "check_unique", "unique_quiet", "universal_quiet")
)

Arguments

names: [character()]
Optional names for each parameter. If NULL, default names and indices are generated.
mean, sd: [double()]
Mean and standard deviation for each marginal normal distribution. sd must be strictly positive.
lower, upper: [double()]
Lower and upper bounds for each parameter. If used with create_normal_prior(), these define the truncation limits.
repair: [character(1)]
Name repair strategy for names. One of "check_unique", "unique", "universal", "unique_quiet", or "universal_quiet". See vctrs::vec_as_names() for details.

Value

An ernest_prior, additionally inheriting from the specialized class uniform_prior or normal_prior.

Details

The provided transformations are vectorized: they accept a matrix of points in the unit hypercube and return a matrix of transformed values.

Examples

# Specify a prior with independent marginals
normal <- create_normal_prior(
  names = c("beta0", "beta1", "beta2"),
  mean = 0,
  sd = 5
)
uniform <- create_uniform_prior(names = "sd", lower = 0, upper = 5)
composite <- normal + uniform
composite
#> composite prior distribution with 4 dimensions (beta0, beta1, beta2, and sd)

# Propose a conditional (hierarchical) prior in vectorized form
fn <- function(x) {
  n <- nrow(x)
  out <- matrix(NA_real_, nrow = n, ncol = 3)
  # x[1] follows N(5, 1)
  out[, 1] <- stats::qnorm(x[, 1], mean = 5, sd = 1)
  # log10(x[2]) follows Uniform(-1, 1)
  out[, 2] <- 10^stats::qunif(x[, 2], min = -1, max = 1)
  # x[3] follows N(x[1], x[2])
  out[, 3] <- stats::qnorm(x[, 3], mean = out[, 1], sd = out[, 2])
  out
}

conditional_prior <- create_prior(
  vectorized_fn = fn,
  names = c("mean", "sd", "x"),
  lower = c(-Inf, 0, -Inf)
)

# Plot the marginals
sample <- conditional_prior$fn(matrix(runif(1000 * 3), nrow = 1000))
hist(sample[, 1], main = "mean")

hist(sample[, 2], main = "sd")

hist(sample[, 3], main = "x")