Department Seminar by Antik Chakraborty

Please Note: This seminar will be given online.

Statistical models for studying biodiversity: challenges and contributions

A healthy ecosystem is essential to our well being and to ensure that we need a better understanding of the life or biodiversity around us. In the rst part of this talk, I will introduce how modern technology is being used to collect data on biodiversity across space and time. These data come in complex forms such as DNA sequences, highdimensional binary vectors, sound signals etc. I will briefly touch upon the statistical challenges involved in making sense of these data. 

In the second part, I will elaborate on a project motivated by studying temporal patterns in bird vocalizations. I will introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed class of FRActional Probit (FRAP) models is based on thresholding of a latent process consisting of an additive expansion of a smooth Gaussian process with a fractional Brownian motion. I will describe a Bayesian approach to inference using Markov chain Monte Carlo. Results from applying the model on Amazon bird vocalization data will be presented which provide substantial evidence for self-similarity and non-Markovian/Poisson dynamics. A hierarchical extension of the proposed model to accommodate vocalizations of multiple birds at the same time will also be discussed.