HMM.parm: Obtain model parameters from a randomly generated HMM

In LuZhangH/floodgate: Floodgate: inference for model-free variable importance

Description

This function obtains model parameters from a randomly generated hidden Markov model

Usage

HMM.parm(p, K_st, M)

Arguments

p	number of nodes
K_st	number of hidden states
M	number of emission states

Value

A list containing pInit: a vector of K_st, which gives the marginal probabilities of the first variable; Q: an array of size (p-1, K_st, K_st), which gives the p-1 transition matrices of the hidden Markov chain; pEmit: an array of size (p, M, K_st), which is the emission probability matrix.

References

\insertRef

sesia2019floodgate

See Also

Other models: Genotypes.parm(), gen.Y()

LuZhangH/floodgate documentation built on Aug. 30, 2020, 2:10 a.m.