bernoulli_beta_model: Run bayesian beta - bernoulli model for estimating proportion...

In eliobartos/misc: Miscellaneous functions

Description

Runs a bayesian estimation of proportion using bernoulli distribution as likelihood and beta distribution as conjugate prior. Posterior distribution is beta distribution.

Usage

bernoulli_beta_model(alpha, beta, success, total, sample_size = 1e+05)

Arguments

alpha	Parameter for prior distribution representing the number of success
beta	Parameter for prior distribution representing the number of fails Prior used is Beta(alpha + 1, beta + 1)
success	Number of success cases in your data
total	Total number of cases in your data
sample_size	Size of sample from posterior distribution

Value

Vector of samples from posterior distribution

Posterior distribution is Beta(alpha + 1 + success, beta + 1 + total - success)

Author(s)

Elio Bartoš

Examples

# No prior information, prior is uniform
post = bernoulli_beta_model(0, 0, 20, 100)

# Prior succes rate is around 5% with estimation strenght as it was estimated on a sample of 100
post2 = bernoulli_beta_model(5, 95, 3, 50)

mean(post)
quantile(post, probs = c(0.05, 0.95)) # 90% highest density posterior interval

mean(post2)
quantile(post2, probs = c(0.05, 0.95))

eliobartos/misc documentation built on Oct. 8, 2021, 1:10 a.m.