mySpec <- hmm(
K = 3, R = 1,
observation = Gaussian(
mu = Gaussian(0, 10),
sigma = Student(mu = 0, sigma = 10, nu = 1, bounds = list(0, NULL))
),
initial = Dirichlet(alpha = c(1, 1, 1)),
transition = Dirichlet(alpha = c(1, 1, 1)),
name = "Univariate Gaussian Hidden Markov Model"
)
set.seed(9000)
y <- as.matrix(
c(rnorm(100, 5, 1), rnorm(100, 0, 1), rnorm(100, -5, 1))
)
explain(mySpec)
myModel <- compile(mySpec)
myFit <- draw_samples(mySpec, myModel, y = y, chains = 1, iter = 500, seed = 9000)
myOpt <- optimizing(mySpec, myModel, y = y, nRun = 50, nCores = 10, seed = 9000)
print(myFit)
# Hard-classify observations based on filtered, smoothed, and Viterbi
classify_alpha(myFit)
classify_gamma(myFit)
classify_zstar(myFit)
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