# update_prior: Calculate posterior distribution of the proportion of liars In truelies: Bayesian Methods to Estimate the Proportion of Liars in Coin Flip Experiments

## Description

update_prior uses the equation for the posterior:

φ(λ | R; N,P) = Pr(R|λ; N,P) φ(λ) / \int Pr(R | λ'; N,P) φ(λ') d λ'

where φ is the prior and Pr(R | λ; N, P) is the probability of R reports of heads given that people lie with probability λ:

Pr(R | λ; N, P) = binom(N, (1-P) + λ P)

## Usage

 1 update_prior(heads, N, P, prior = stats::dunif, npoints = 1000) 

## Arguments

 heads Number of good outcomes reported N Total number in sample P Probability of bad outcome prior Prior over lambda. A function which takes a vector of values between 0 and 1, and returns the probability density. The default is the uniform distribution. npoints How many points to integrate on?

## Value

The probability density of the posterior distribution, as a one-argument function.

## Examples

 1 2 posterior <- update_prior(heads = 30, N = 50, P = 0.5, prior = stats::dunif) plot(posterior) 

truelies documentation built on Aug. 27, 2019, 1:02 a.m.