This chapter deals with onomastic homonymy as a phenomenon of ancient Greek literary history. Focusing first on early Greek poets about whom ancient testimonies claim there were doublets (Euenus, Xenophanes, Alcman and Sappho), the chapter moves on to examine doublets of poets emerging in the Parian Chronicle (Simonides, Sosiphanes, Stesichorus, Melanippides), to conclude with the Phocian Homer of Byzantine scholarship (Tzetzes). After distinguishing between historical homonyms and scholarly constructs, the chapter examines the possible reasons behind the duplication of poets, most particularly the need to deal with conflicting details in the transmitted biographies while preserving the textual tradition.

This chapter discusses additional considerations pertaining to the hypothesis set, prior probabilities, and Occam factors, which mediate the trade-offs between parsimony and accuracy in Bayesian analysis.

The concept of Occam’s Razor, also known as the principle of parsimony, is a motivating force in science. Galileo’s experiment of dropping objects of different weights from atop the Leaning Tower of Pisa to show that they fall at the same rate is an illustration of this principle. But the application of Occam’s Razor to climate modeling is less straightforward. Simple models of the kind used by Manabe provide qualitative insights, but they are not well-suited for quantitative predictions. To understand this, we can make an analogy between the hierarchy of climate models and the hierarchy of biological models, from fruit fly to mouse. Simple models are used to explore “climate tipping points,” where amplifying feedbacks lead to abrupt climate change, but they may not consider all the stabilizing feedbacks. It is therefore important to use a hierarchy of models, with varying degrees of complexity, to study climate phenomena.

This chapter is devoted to parameter estimation. We first discuss the physical dependence of CMB anisotropies on cosmological parameters. After a section on CMB data we then treat in some detail statistical methods for CMB data analysis. We discuss especially the Fisher matrix and explain Markov chain Monte Carlo methods. We also address degeneracies, combinations of cosmological parameters on which CMB anisotropies and polarization depend only weakly. Because of these degeneracies, cosmological parameter estimation also makes use of other, non CMB related, observations especially observations related to the large scale matter distribution. We summarize them and other cosmological observations in two separate sections.