Prof. Akaike made significant contributions in various fields of statistical science, in particular, in time series analysis in frequency domain and time domain, information criterion and Bayes modeling. In this article, his research contributions are described in order of launching period, frequency time domain analysis, time domain time series modeling, AIC and statistical modeling, and Bayes...

Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our...

Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging yet common problem in pattern recognition. Hierarchical models allow sharing of statistical strength across groups while preserving group-specific idiosyncrasies, and are commonly used for modeling such grouped data [3]. We develop flexible hierarchical Bayesian models that parameter...

The Bayesian interpretation of probability is one of two broad categories of interpretations. Bayesian inference updates knowledge about unknowns, parameters, with information from data. The LaplacesDemon package in R enables Bayesian inference, and this vignette provides an introduction to the topic. This article introduces Bayes’ theorem, model-based Bayesian inference, components of Bayesian...

We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function, the H-loss, implementing the simple intuition that additional mistakes in the subtree of a mistaken class should not be charged for. Based on a probabilistic data model introduced in earlier work, we...

Hierarchical classification is supervised multi-class classification problem over the set of class labels organized according to a hierarchy. In this report, we study the work by Ramaswamy et. al. on hierarchical classification over symmetric tree distance loss. We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss. We design a $\mathcal{O}(nk\log...