get_prior: Construct Prior Parameters for BKP/DKP Models

In NBKP: Beta Kernel Process Modeling with Negative Binomial Response

Construct Prior Parameters for BKP/DKP Models

Description

Computes prior parameters for Beta Kernel Process (BKP, binary) or Dirichlet Kernel Process (DKP, multi-class) models. Supports three prior strategies: noninformative, fixed, adaptive.

Usage

get_prior(
  prior = c("noninformative", "fixed", "adaptive"),
  model = c("BKP", "DKP"),
  r0 = 2,
  p0 = NULL,
  y = NULL,
  m = NULL,
  Y = NULL,
  K = NULL
)

Arguments

prior	Character string; prior type. One of: '"noninformative"', '"fixed"', '"adaptive"'.
model	Character string; model type. One of: '"BKP"' (binary), '"DKP"' (multi-class).
r0	Numeric; prior precision (positive scalar, default = 2).
p0	Numeric; global prior mean. BKP: scalar in (0,1); DKP: vector summing to 1.
y	Numeric vector; observed success counts (BKP only).
m	Numeric vector; number of trials (BKP only, same length as 'y').
Y	Numeric matrix; observed class counts (DKP only, n × q).
K	Numeric matrix; precomputed kernel matrix.

Details

Prior strategies: * 'noninformative': flat prior (Beta(1,1) or Dirichlet(1,...,1)). * 'fixed': global constant prior. * 'adaptive': kernel-smoothed local prior, estimated from observed data.

Value

For BKP: a list with 'alpha0' and 'beta0'. For DKP: a matrix 'alpha0' of prior Dirichlet parameters.

Examples

# BKP example
set.seed(123)
n <- 10
X <- matrix(runif(n*2), ncol = 2)
y <- rbinom(n, size = 5, prob = 0.6)
m <- rep(5, n)
K <- matrix(1, n, n)
prior_bkp <- get_prior(
  model = "BKP", prior = "adaptive",
  r0 = 2, y = y, m = m, K = K
)

NBKP documentation built on June 18, 2026, 1:06 a.m.