Spatio-temporal data analysis, Adaptive MCMC, Wavelet analysis
Publications
Journal papers
Takamitsu Araki, Shotaro Akaho, “Spatially multi-scale dynamic factor modeling via sparse estimation”, International Journal of Mathematics for Industry, Vol.11, No.1, pp.1950005, 2019. [Open Access]
Takamitsu Araki, Tadafumi Ochi, Norio Matsumoto, Shotaro Akaho, “Simultaneous estimation of spatio-temporal distribution and duration of slow slip event by switching model”, Journal of Signal Processing, Vol.21, No.6, pp.297-308, 2017.
Takamitsu Araki, Kazushi Ikeda, “Adaptive Markov Chain Monte Carlo for Auxiliary Variable Method and Its Application to Parallel Tempering”, Neural Networks, Vol.43, pp.33–40, 2013.
Conferences
Takamitsu Araki, Kazushi Ikeda, “Adaptive Gibbs Variable Selection and Its Generalization to Auxiliary Variable Framework”, Bayesian Inference and Stochastic Computation 2012 workshop, Tachikawa, Tokyo, Japan, June 2012.
Takamitsu Araki, Hideitsu Hino, Shotaro Akaho, “A kernel method to extract common features based on mutual information”, International Conference on Neural Information Processing (ICONIP2014), Sarawak, Malaysia, November 2014.
Takamitsu Araki, Tadafumi Ochi, Norio Matsumoto, Shotaro Akaho, “Robust estimation of spatio-temporal distribution of slow slip event by switching model”, Spatial Statistics 2015, Avignon, France, June 2015.
Takamitsu Araki, Shotaro Akaho “Sparse spatial dynamic factor model with basis expansion”, Joint Statistical Meetings 2016, Chicago, USA, July 2016.
Takamitsu Araki, Shotaro Akaho “Dynamic factor modeling with spatially multi-scale structures for spatio-temporal data”, Statistical Data Science 2017, London, UK, July 2017.
Takamitsu Araki, Ryosaku Ikeda, Van Q. Doan, Noriko N. Ishizaki and Hiroyuki Kusaka “Correction method with additive model for NWP-based wind speed forecast”, 17th Wind Integration Workshop, Stockholm, Sweden, October 2018.