integer. Number of mixture components. Set a large enough number
because the algorithm identifies major dependency patterns from
the data via the sparse mixture model.
rho
double. Constant that multiplies the penalty term. An optimal
value should be determined together with the threshold on the
anomaly score, so the performance of anomaly detection is maximized.
m0
a numeric vector. Location parameter of Gauss-Laplace prior.
Keep default if no prior information is available.
lambda0
double. Coefficient for scale parameter of Gauss-Laplace
prior. Keep default if no prior information is available.
alpha
double. Concentration parameter of Dirichlet prior.
Keep default if no prior information is available.
pi_threshold
double. Threshold to decide a number of states.
If pi < pi_threshold, the states are rejected in the sense of
sparse estimation.