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| Paper: |
Bayesian Model Selection for LISA Pathfinder |
| Volume: |
467, 9th LISA Symposium |
| Page: |
337 |
| Authors: |
Karnesis, N.; Nofrarias, M.; Sopuerta, C. F.; Gibert, F.; Lobo, A. |
| Abstract: |
The LISA Pathfinder mission (LPF) aims at validating the displacement and acceleration noise models and to test key technologies for the future LISA mission. The LISA Technology Package (LTP) Data Analysis team has developed complex models of the LTP for simulations and data analysis during the mission. These models contain a large number of parameters to be estimated and for that reason, we need to recover only the essential ones that describe the observations. Being able to distinguish between competing models that describe the data introduces many possible applications in LTP Data Analysis. In our analysis we use two main different approximations to compute the Bayes Factor: the Reversible Jump Markov Chain Monte Carlo (RJMCMC) and the Laplace approximations. They are applied first to toy models and then verified with full LTP models. This work is part of the LTPDA Matlab toolbox. |
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