MultiBD (version 0.2.0)

bbd_prob: Transition probabilities of a birth/birth-death process

Description

Computes the transition pobabilities of a birth/birth-death process using the continued fraction representation of its Laplace transform

Usage

bbd_prob(t, a0, b0, lambda1, lambda2, mu2, gamma, A, B, nblocks = 256,
  tol = 1e-12, computeMode = 0, nThreads = 4, maxdepth = 400)

Value

a matrix of the transition probabilities

Arguments

t

time

a0

total number of type 1 particles at t = 0

b0

total number of type 2 particles at t = 0

lambda1

birth rate of type 1 particles (a two variables function)

lambda2

birth rate of type 2 particles (a two variables function)

mu2

death rate function of type 2 particles (a two variables function)

gamma

transition rate from type 2 particles to type 1 particles (a two variables function)

A

upper bound for the total number of type 1 particles

B

upper bound for the total number of type 2 particles

nblocks

number of blocks

tol

tolerance

computeMode

computation mode

nThreads

number of threads

maxdepth

maximum number of iterations for Lentz algorithm

References

Ho LST et al. 2015. "Birth(death)/birth-death processes and their computable transition probabilities with statistical applications". In review.

Examples

if (FALSE) {
data(Eyam)

# (R, I) in the SIR model forms a birth/birth-death process

loglik_sir <- function(param, data) {
  alpha <- exp(param[1]) # Rates must be non-negative
  beta  <- exp(param[2])
  N <- data$S[1] + data$I[1] + data$R[1]
  
  # Set-up SIR model with (R, I)
  
  brates1 <- function(a, b) { 0 }
  brates2 <- function(a, b) { beta  * max(N - a - b, 0)  * b }
  drates2 <- function(a, b) { 0 }
  trans21 <- function(a, b) { alpha * b }
  
  sum(sapply(1:(nrow(data) - 1), # Sum across all time steps k
             function(k) {
               log(
                 bbd_prob( # Compute the transition probability matrix
                   t  = data$time[k + 1] - data$time[k], # Time increment
                   a0 = data$R[k], b0 = data$I[k],       # From: R(t_k), I(t_k)
                   brates1, brates2, drates2, trans21,
                   A = data$R[k + 1], B = data$R[k + 1] + data$I[k] - data$R[k],
                   computeMode = 4, nblocks = 80         # Compute using 4 threads
                 )[data$R[k + 1] - data$R[k] + 1, 
                   data$I[k + 1] + 1]                    # To: R(t_(k+1)), I(t_(k+1))
               )
             }))
}

loglik_sir(log(c(3.204, 0.019)), Eyam) # Evaluate at mode
}