Bayesian Calibrated Significance Levels Applied to the Spectral Tilt and Hemispherical Asymmetry

Understand

Bayesian model selection provides a formal method of determining the level of support for new parameters in a model.

• However, if there is not a specific enough underlying physical motivation for the new parameters it can be hard to assign them meaningful priors, an essential ingredient of Bayesian model selection.
• Here we look at methods maximizing the prior so as to work out what is the maximum support the data could give for the new parameters.
• If the maximum support is not high enough then one can confidently conclude that the new parameters are unnecessary without needing to worry that some other prior may make them significant.