PMF Handlers

Functions for probability mass function handling. More...

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Functions
vector	discretised_pmf (vector params, int n, int dist)

Description

Functions for probability mass function handling.

Functions from pmfs.stan for handling and manipulating probability mass functions.

Function Documentation

discretised_pmf()

vector discretised_pmf	(	vector	params,
		int	n,
		int	dist )

#include </github/workspace/inst/stan/functions/pmfs.stan>

Probability Mass Function (PMF) Utilities

This file contains functions for creating and manipulating probability mass functions, particularly for discretizing continuous distributions for use in delay modeling. Discretise a continuous distribution

This function discretizes continuous distributions (lognormal or gamma) to create a probability mass function over discrete time points (days). Adapted from https://github.com/epiforecasts/epinowcast (MIT License, copyright: epinowcast authors)

Parameters

params	Vector of distribution parameters ([mu, sigma] for lognormal or [shape, rate] for gamma)
n	Number of days to calculate PMF for
dist	Distribution type (0: lognormal, 1: gamma)

Returns

A vector of length n containing the discretized probability mass function

Definition at line 26 of file pmfs.stan.

                                                       {
  vector[n] lpmf;
  vector[n] upper_lcdf;
  if (dist == 0) {
    for (i in 1:n) {
      upper_lcdf[i] = lognormal_lcdf(i | params[1], params[2]);
    }
  } else if (dist == 1) {
    for (i in 1:n) {
      upper_lcdf[i] = gamma_lcdf(i | params[1], params[2]);
    }
  } else {
    reject("Unknown distribution function provided.");
  }
  // discretise
  if (n > 1) {
    lpmf[1] = upper_lcdf[1];
    lpmf[2] = upper_lcdf[2];
    if (n > 2) {
      lpmf[3:n] = log_diff_exp(upper_lcdf[3:n], upper_lcdf[1:(n - 2)]);
    }
    // normalize
    lpmf = lpmf - log_sum_exp(upper_lcdf[(n - 1):n]);
  } else {
    lpmf[1] = 0;
  }
  return(exp(lpmf));
}

Referenced by get_delay_rev_pmf().

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