Hierarchical Bayesian Aldrich–McKelvey Scaling

Evaluating scaling models in biology using hierarchical Bayesian approaches

Theoretical models for allometric relationships between organismal form and function are typically tested by comparing a single predicted relationship with empirical data. Several prominent models, however, predict more than one allometric relationship, and comparisons among alternative models have not taken this into account. Here we evaluate several different scaling models of plant morpholog...

Bayesian Hierarchical Models 1 Running head: BAYESIAN HIERARCHICAL MODELS Bayesian Hierarchical Models

Bayesian Hierarchical Community Discovery

We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks. Our model is a tree-structured mixture of potentially exponentially many stochastic blockmodels. We describe a family of greedy agglomerative model selection algorithms that take just one pass through the data to learn a fully probabilistic, hierarchical community model. In ...

Bayesian Hierarchical Reinforcement Learning

We describe an approach to incorporating Bayesian priors in the MAXQ framework for hierarchical reinforcement learning (HRL). We define priors on the primitive environment model and on task pseudo-rewards. Since models for composite tasks can be complex, we use a mixed model-based/model-free learning approach to find an optimal hierarchical policy. We show empirically that (i) our approach resu...

Bayesian Hierarchical Cross-Clustering

Most clustering algorithms assume that all dimensions of the data can be described by a single structure. Cross-clustering (or multiview clustering) allows multiple structures, each applying to a subset of the dimensions. We present a novel approach to crossclustering, based on approximating the solution to a Cross Dirichlet Process mixture (CDPM) model [Shafto et al., 2006, Mansinghka et al., ...