Forward algorithm for homogeneous hidden semi-Markov models
forward_hsmm.Rd
Calculates the (approximate) log-likelihood of a sequence of observations under a homogeneous hidden semi-Markov model using a modified forward algorithm.
Arguments
- dm
list of length N containing vectors of dwell-time probability mass functions (PMFs) for each state. The vector lengths correspond to the approximating state aggregate sizes, hence there should be little probablity mass not covered by these.
- omega
matrix of dimension c(N,N) of conditional transition probabilites, also called embedded transition probability matrix.
Contains the transition probabilities given that the current state is left. Hence, the diagonal elements need to be zero and the rows need to sum to one. Can be constructed using
tpm_emb
.- allprobs
matrix of state-dependent probabilities/ density values of dimension c(n, N) which will automatically be converted to the appropriate dimension.
- trackID
optional vector of length n containing IDs
If provided, the total log-likelihood will be the sum of each track's likelihood contribution. In this case,
dm
can be a nested list, where the top layer contains kdm
lists as described above.omega
can then also be an array of dimension c(N,N,k) with one conditional transition probability matrix for each track. Furthermore, instead of a single vectordelta
corresponding to the initial distribution, adelta
matrix of initial distributions, of dimension c(k,N), can be provided, such that each track starts with it's own initial distribution.- delta
optional vector of initial state probabilities of length N
By default, the stationary distribution is computed (which is typically recommended).
- eps
small value to avoid numerical issues in the approximating transition matrix construction. Usually, this should not be changed.
- report
logical, indicating whether initial distribution, approximating transition probability matrix and
allprobs
matrix should be reported from the fitted model. Defaults toTRUE
.
Details
Hidden semi-Markov models (HSMMs) are a flexible extension of HMMs, where the state duration distribution is explicitly modelled by a distribution on the positive integers. For direct numerical maximum likelhood estimation, HSMMs can be represented as HMMs on an enlarged state space (of size \(M\)) and with structured transition probabilities.
This function is designed to be used with automatic differentiation based on the R
package RTMB
. It will be very slow without it!
See also
Other forward algorithms:
forward()
,
forward_g()
,
forward_ihsmm()
,
forward_p()
,
forward_phsmm()