segmentation/bmoss/change_detec.py
def parameters(self):
        """Return the posterior parameters.

        All the information content of the data is summarized by the sufficient
        statistics. As a result the posterior parameters are a function of the
        sufficient statistics. This is a consequence of the conjugacy of the
        matrix-variate Gaussian-inverse-Gamma distribution.

        """

        m = self.__m
        s = linalg.cholesky(self.__prod).transpose()
        w = self.__weight

        # Compute the parameters of the posterior distribution.
        return linalg.solve(s[:m, :m], s[:m, m:]), \
               np.dot(s[:m, :m].transpose(), s[:m, :m]), \
               np.dot(s[m:, m:].transpose(), s[m:, m:]) / w, \
               w
Return the posterior parameters.

        All the information content of the data is summarized by the sufficient
        statistics. As a result the posterior parameters are a function of the
        sufficient statistics. This is a consequence of the conjugacy of the
        matrix-variate Gaussian-inverse-Gamma distribution.

        
Return the posterior parameters.
All the information content of the data is summarized by the sufficient
statistics. As a result the posterior parameters are a function of the
sufficient statistics. This is a consequence of the conjugacy of the
matrix-variate Gaussian-inverse-Gamma distribution.
Return the posterior parameters . All the information content of the data is summarized by the sufficient statistics . As a result the posterior parameters are a function of the sufficient statistics . This is a consequence of the conjugacy of the matrix - variate Gaussian - inverse - Gamma distribution .
def parameters(self):
        m = self.__m
        s = linalg.cholesky(self.__prod).transpose()
        w = self.__weight
        return linalg.solve(s[:m, :m], s[:m, m:]), \
               np.dot(s[:m, :m].transpose(), s[:m, :m]), \
               np.dot(s[m:, m:].transpose(), s[m:, m:]) / w, \
               w
def parameters ( self ) : m = self . __m s = linalg . cholesky ( self . __prod ) . transpose ( ) w = self . __weight return linalg . solve ( s [ : m , : m ] , s [ : m , m : ] ) , np . dot ( s [ : m , : m ] . transpose ( ) , s [ : m , : m ] ) , np . dot ( s [ m : , m : ] . transpose ( ) , s [ m : , m : ] ) / w , w
Return the posterior parameters.
Return the posterior parameters .
"""Return the posterior parameters.

        All the information content of the data is summarized by the sufficient
        statistics. As a result the posterior parameters are a function of the
        sufficient statistics. This is a consequence of the conjugacy of the
        matrix-variate Gaussian-inverse-Gamma distribution.

        """ # Compute the parameters of the posterior distribution.
self