Create a Markov-modulated Hawkes Process(MMHP) model according to the given parameters: lambda0, lambda1, alpha, beta, event times and transition probability matrix. If event time events is missing, then it means that data will be added later(e.g. simulated)

pp_mmhp(lambda0, lambda1, alpha, beta, Q = NULL, delta = NULL, events = NULL)

Arguments

lambda0

intensity for homogeneous Poisson process.

lambda1

base intensity for Hawkes process.

alpha

jump size of the increase in intensity in the hawkes process

beta

exponential decrease of intensity in the hawkes process

Q

transition probability matrix.

delta

initial state probability.

events

vector containing the event times. Note that the first event is at time zero. Alternatively, events could be specified as NULL, meaning that the data will be added later (e.g. simulated).

Value

mmhp object

Examples

Q <- matrix(c(-0.4, 0.4, 0.2, -0.2), ncol = 2, byrow = TRUE) pp_mmhp(Q, delta = c(1 / 3, 2 / 3), lambda0 = 0.9, lambda1 = 1.1, alpha = 0.8, beta = 1.2 )
#> Markov Modulated Hawkes Process #> lambda0 0.9 #> lambda1 1.1 #> alpha 0.8 #> beta 1.2 #> Q -0.4 0.2 0.4 -0.2 #> delta 0.3333333 0.6666667