stationary_mle | R Documentation |
Maximum-likelihood estimation of stationary distribution π based on (a) a sampled trajectory z of a model-indicator variable or (b) a sampled transition count matrix N.
stationary_mle(z, N, labels, method = "rev", abstol = 1e-05, maxit = 1e+05)
z |
MCMC output for the discrete indicator variable with numerical,
character, or factor labels (can also be a |
N |
the observed |
labels |
optional: vector of labels for complete set of models
(e.g., models not sampled in the chain |
method |
Different types of MLEs:
|
abstol |
absolute convergence tolerance (only for |
maxit |
maximum number of iterations (only for |
The estimates are implemented mainly for comparison with the Bayesian sampling approach implemented in stationary
, which quantify estimation uncertainty (i.e., posterior SD) of the posterior model probability estimates.
a vector with posterior model probability estimates
Trendelkamp-Schroer, B., Wu, H., Paul, F., & Noé, F. (2015). Estimation and uncertainty of reversible Markov models. The Journal of Chemical Physics, 143(17), 174101. https://doi.org/10.1063/1.4934536
stationary
P <- matrix(c(.1,.5,.4, 0,.5,.5, .9,.1,0), ncol = 3, byrow=TRUE) z <- rmarkov(1000, P) stationary_mle(z) # input: transition frequency tab <- transitions(z) stationary_mle(N = tab)
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