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def compute_loglikelihood(self):
#Init the list of loglikelihhod values, one value for each y observation
ll = []
for t in range(len(self.y)):
prob_y_t = 0
mu_t = 0
for j in range(self.k_regimes):
#To use the law of total probability, uncomment this row and comment out the next
# two rows
#prob_y_t += poisson.pmf(y[t], mu[t][j]) * self.delta_matrix[t][j]
#Calculate the Poisson mean mu_t as an expectation over all Markov state
# probabilities
mu_t += self.mu_matrix[t][j] * self.delta_matrix[t][j]
prob_y_t += poisson.pmf(self.y[t], mu_t)
#This is a bit of a kludge. If the likelihood turns out to be real tiny, fix it to
# the EPS value for the machine
if prob_y_t < self.EPS:
prob_y_t = self.EPS
#Push the LL into the list of LLs
ll = np.array(ll)
return ll
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