A Bayesian Approach to the Understanding of Exoplanet Populations and the Origin of Life

The study of extrasolar planets, or exoplanets for short, has developed rapidly over the last decade. While we have spent much effort building both ground-based and space telescopes to search for exoplanets, it is even more important that we use the observational data wisely to understand them. Exoplanets are of great interest to both astronomers and the general public because they have shown varieties of characteristics that we couldn't have anticipated from planets within our Solar System. To properly analyze the exoplanet populations, we need the tools of statistics. Therefore, in Chapter 1, I describe the science background as well as the statistical methods which will be applied in this thesis. In Chapter 2, I discuss how to train a hierarchical Bayesian model in detail to fit the relationship between masses and radii of exoplanets and categorize exoplanets based on that. A natural application that comes with the model is to use it for future observations of mass/radius and predict the other measurement. Thus I will show two application cases in Chapter 3. Composition of an exoplanet is also very much constrained by its mass and radius. I will show an easy way to constrain the