This function updates the class weight vector by drawing from its posterior distribution.

## Usage

update_s(delta, m)

## Arguments

delta

A numeric for the concentration parameter vector rep(delta,C) of the Dirichlet prior for s. Per default, delta = 1. In case of Dirichlet process-based updates of the latent classes, delta = 0.1 per default.

m

The vector of current class frequencies.

## Value

A vector, a draw from the Dirichlet posterior distribution for s.

## Details

Let $$m=(m_1,\dots,m_C)$$ be the frequencies of $$C$$ classes. Given the class weight (probability) vector $$s=(s_1,\dots,s_C)$$, the distribution of $$m$$ is multinomial and its likelihood is $$L(m\mid s) \propto \prod_{i=1}^C s_i^{m_i}.$$ The conjugate prior $$p(s)$$ for $$s$$ is a Dirichlet distribution, which has a density function proportional to $$\prod_{i=1}^C s_i^{\delta_i-1},$$ where $$\delta = (\delta_1,\dots,\delta_C)$$ is the concentration parameter vector. Note that in RprobitB, $$\delta_1=\dots=\delta_C$$. This restriction is necessary because the class number $$C$$ can change. The posterior distribution of $$s$$ is proportional to $$p(s) L(m\mid s) \propto \prod_{i=1}^C s_i^{\delta_i + m_i - 1},$$ which in turn is proportional to a Dirichlet distribution with parameters $$\delta+m$$.

## Examples

### number of classes
C <- 4
### current class sizes
m <- sample.int(C)
### concentration parameter for Dirichlet prior (single-valued)
delta <- 1
### updated class weight vector
update_s(delta = 1, m = m)
#>           [,1]
#> [1,] 0.1839418
#> [2,] 0.1893315
#> [3,] 0.2454951
#> [4,] 0.3812317