Estimates P(S = s_k; \mathbf{W}), k = 1, …, K, the probability of being in state s_k using the weight matrix \mathbf{W}.
These probabilites can be marginal (P(S = s_k;
\mathbf{W})) or conditional (P(S = s_k \mid
\ell^{}, \ell^{+}; \mathbf{W})), depending on the
provided information (pdfs$PLC
and
pdfs$FLC
).
If both are NULL
then estimate_state_probs
returns a vector of
length K with marginal probabilities.
If
either of them is not NULL
then it returns an
N \times K matrix, where row i is the
probability mass function of PLC i being in state
s_k, k = 1, …, K.
1 2 
weight.matrix 
N \times K weight matrix 
states 
vector of length N with entry i being the label k = 1, …, K of PLC i 
pdfs 
a list with estimated pdfs for PLC and/or FLC evaluated at each PLC, i=1, …, N and/or FLC, i=1, …, N 
num.states 
number of states in total. If

A vector of length K or a N \times K matrix.
1 2 3  WW < matrix(runif(10000), ncol = 10)
WW < normalize(WW)
estimate_state_probs(WW)

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