Likelihood
For the likelihood, we mostly follow the approach of Bodin et al. (2012). Summary of the definition is as follows:
- Likelihood is defined by multivariate Gaussian distribution
,
where the superscript j is an index for traces.
-
We use noise covariance matrix with r^2 decay
- σ_j is treated as a model parameter
- r_j is automatically determined by the choice of the low-pass filter.
- The inverse of C is calculated using singular value decomposition.
- Computing the determinant of C is not necessary because it is canceled out in the case of MCMC.
For the following notations, please refer to Input files.
- N_TRC
- SIG_MIN, SIG_MAX