TRAFST Vol.8 No.1 pp. 14-21


The Birth of Statistical Mathematics and its Expanding World

Tomoyuki HIGUCHI

The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa-shi, Tokyo

16 February 2014
26 February 2014
April 15, 2014
statistical modeling, information criterion, prediction performance, Bayesian modeling,MCMC, particle filter, personalization, induction and deduction, simulation, data assimilation, uncertaintyquantification, machine learning, sparse modeling, kernel method

This document gives a brief explanation for what is “Statistical Mathematics,” and describes how its notion has been expanding to a wide variety of research fields. The recent interests of statistical mathematics are focused on generating more flexible mathematical models for describing complex phenomena in terms of data generation mechanism and/or its function. Such efforts demand collaborative works with the machine learning community in a big data era which we are now faced with.

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Last updated on Feb. 24, 2017