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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def state_likelihood_markov_blanket(self, beta, alpha, col_no):
 """ Returns Markov blanket of the states given the variance latent variables Parameters beta : np.array Contains untransformed starting values for latent variables alpha : np.array State matrix Returns State likelihood """
     
        _, _, _, Q = self._ss_matrices(beta)
        blanket = np.append(0,ss.norm.logpdf(alpha[col_no][1:]-alpha[col_no][:-1],loc=0,scale=np.sqrt(Q[col_no][col_no])))
        blanket[:-1] = blanket[:-1] + blanket[1:]
        return blanket