# Sortostat/Optimal sorting cutoffs

## Contents

## Problem

What is the optimal cut-off percentile for choosing a chamber to be sorted if you have N sorts (trials) remaining until you must take the sort to preserve a constant dilution rate?

## Analytical Solution

### Definition of variables

[math]\emph E[X_N] = [/math] expected value of the optimal percentage that can be returned from N trials

[math]\emph S_i = [/math] random variable representing the percentile returned from the ith trial

- all trials are assumed to be independent therefore [math]\emph S_i = S[/math], for all i

[math]\emph C_i = [/math] the cut-off percentile for the ith trial.

### General

[math]\emph E[X_N] = P(S\gt C_1) E[S|S\gt C_1] + [/math]

[math]\emph (1-P(S\gt C_1))(P(S\gt C_2)E[S|S\gt C_2]) + [/math]

[math]\emph (1-P(S\gt C_1))(1-P(S\gt C_2))(P(S\gt C_3)E[S|S\gt C_2]) + [/math]

[math]\emph ... [/math]

[math]\emph (1-P(S\gt C_1))(1-P(S\gt C_2))...(1-P(S\gt C_{N-1})E[S_N] [/math]

### Simplified

Since [math]\emph (1-P(S\gt C_1))[/math] can be factored out of every term after the first above, the solution can be simplified and solved recursively:

[math]\emph E[X_N] = P(S\gt C_N) E[S|S\gt C_N] + (1-P(S\gt C_N))E[X_{N-1}][/math]

base case:

[math]\emph E[X_1] = \int_0^\infty P(S)*S dS[/math]

- e.g., if you have only 1 trial then you expect to get the mean of the distribution for S.

## Simulation Solution

Since our probability skills were pretty sad, we (Alex Mallet) simulated it to confirm our analytical results. MATLAB file can be found here.