Learning Large-Scale Bayesian Networks with the sparsebn Package

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure

Greedy equivalence search for nonparametric graphical models

One of the hallmark achievements of the theory of graphical models and Bayesian model selection is the celebrated greedy equivalence search (GES) algorithm due to Chickering and Meek. GES is known to consistently estimate the structure of directed acyclic graph (DAG) models in various special cases including Gaussian and discrete models, which are in particular curved exponential families. A general theory that covers general nonparametric DAG models, however, is missing. Here, we establish the consistency of greedy equivalence search for general families of DAG models that satisfy smoothness conditions on the Markov factorization, and hence may not be curved exponential families, or even parametric. The proof leverages recent advances in nonparametric Bayes to construct a test for comparing misspecified DAG models that avoids arguments based on the Laplace approximation. Nonetheless, when the Laplace approximation is valid and a consistent scoring function exists, we recover the classical result. As a result, we obtain a general consistency theorem for GES applied to general DAG models

The inflationary universe: a primordial bridge between fundamental theory and observable phenomena

This dissertation encapsulates three connected projects that I have worked on as a graduate student. I begin with an introduction to the beautiful intersection of fundamental physics and the primordial universe that is inflationary cosmology. I then present the three main chapters of this dissertation. The first explores the possibility of realizing inflation in steep potentials via multi-field dynamics, which draws on motivations from string theory and the Swampland program. The second chapter examines multi-field inflation at a more fundamental level by rigorously constructing the types of solutions we can achieve in models with features expected in UV-complete theories. The last chapter shifts focus to the gravitational wave signatures that multi-field models of inflation exhibit, with special emphasis on the conditions for these signatures to be observable in upcoming gravitational wave observatories.Physic