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# Common Specialized Basic Subjects

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## Introduction to Statistical Modeling Ⅰ　／　Introduction to Statistical Modeling Ⅱ

 ＜Introduction to Statistical Modeling Ⅰ＞ Data obtained from the real world are diverse and complicated, and the characteristics change over time, and the amount of data also becomes huge. It is required not to classify such data but to clarify basic characteristics of information sources and to construct effective analysis methods based on modeling. In this class, students learn the basis of statistical science including the stochastic process, the probability distribution, and Bayesian theory, and then learn nonstationary signal analysis method which is used in field data. Furthermore, we consider signal generation and control in the lives, especially neural system. ＜Introduction to Statistical Modeling Ⅱ＞Statistical modelling and analysis methods are covered in this course. In particular, the subjects about time-series analysis, spatial data analysis, and inverse analysis based on Bayesian estimation methods will be discussed.

## Introduction to Data Science Ⅰ　／　Introduction to Data Science Ⅱ

 ＜Introduction to Data Science Ⅰ＞ This course deals with practical data analysis methods widely applied in scientific investigation and research, involving practices using statistical software R or SAS. ＜Introduction to Data Science Ⅱ＞The lectures are centered around information theory and statistics， covering statistical models， likelihood， maximum likelihood method， entropy and information quantity， Akaike information criterion， and model evaluation.

## Introduction to Statistical Inference Ⅰ　／　Introduction to Statistical Inference Ⅱ

 ＜Introduction to Statistical Inference Ⅰ＞ The lectures in this subject explore fundamental concepts relating to theories of statistical inference. More specifically, the subject covers the fundamentals of probability theory, statistical inference theory, asymptotic theory, linear models, and Bayesian statistics.＜Introduction to Statistical Inference Ⅱ＞

## Computational Methodology in Statistical Inference Ⅰ　／　Computational Methodology in Statistical Inference Ⅱ

 ＜Computational Methodology in Statistical Inference Ⅰ＞ The lectures cover fundamentals of computational inference such as applied/numerical linear algebra, matrix differential calculus, large-scale linear computing, theory and algorithms of optimization, state space representations of dynamical systems and canonical forms. ＜Computational Methodology in Statistical Inference Ⅱ＞This course deals with statistical models in machine learning and computational methodologies for treating such models. Topics include graphical modeling， hidden Markov model， hierarchical Bayesian models， EM algorithms， variational Bayesian algorithms， and Markov chain Monte Carlo methods.

★  Other departments in the School of Multidisciplinary Sciences (Department of Polar Science and Department of Informatics) offer the Common Specialized Basic Subjects below.