Dr. Luigi Spezia - BioMathematics and Statistics Scotland |

Fri 08 Feb 2019, 15:05 - 16:00 |

JCMB 5323 |

If you have a question about this talk, please contact: Ruben Amoros Salvador (ramoros)

Bayesian inference on the covariance matrix is usually performed after placing an Inverse-Wishart or a multivariate Jeffreys as a prior density, but both of them, for different reasons, present some drawbacks. As an alternative, the covariance matrix can be modelled by separating out the standard deviations and the correlations. This separation strategy takes advantage of the fact that usually it is more straightforward and flexible to set priors on the standard deviations and the correlations rather than on the covariance matrix. On the other hand, the priors must preserve the positive definiteness of the correlation matrix. This can be obtained by considering the Cholesky decomposition of the correlation matrix, whose entries are reparameterized using trigonometric functions. The efficiency of the trigonometric separation strategy is shown through applications to hidden Markov models and autoregressive processes.