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TRAFST Vol.8 No.1 pp. 14-21

Review:

The Birth of Statistical Mathematics and its Expanding World

Tomoyuki HIGUCHI

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

Received:
16 February 2014
Accepted:
26 February 2014
Published:
April 15, 2014
Keywords:
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
Abstract

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