Department's Special Subjects
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Monte Carlo algorithms and stochastic simulation ／ Yukito Iba / Professor
This course deals with Markov Chain Monte Carlo (MCMC) and other stochastic algorithms with real world applications.
Modeling of complex hierarchical structures ／ Yukito Iba / Professor
This course focused on statistical modeling of complex and hierarchical systems.
Special Course on Data Assimilation Ⅰ ／ Genta Ueno / Professor
This is a course of seminar and practice on sequential data assimilation methods such as the ensemble Kalman filter. On the basis of the statespace model, students derive the sequential methods and implement the procedure.
Special Course on Data Assimilation Ⅱ ／ Genta Ueno / Professor
This is a course of seminar and practice on variational data assimilation methods such as the adjoint method. On the basis of maximum a posteriori (MAP) estimation of the statespace model, students derive the variational methods and implement the procedure.
Special Topics in Time Series Analysis Ⅰ ／ Yoshinori Kawasaki / Professor
This course will cover vector autoregressions and their applications to causal analysis of time series. To extend the arguments to nonstationary time series, after learning unit root tests to check the persistency of time series, we will proceed to the testing and estimation of cointegrated systems.
Special Topics in Time Series Analysis Ⅱ ／ Yoshinori Kawasaki / Professor
This course will cover various types of smoothing algorithms for statespace models. We will observe how the the initialization of filter affects the likelihood evaluation. Based on the knowledge of various smoothing algorithm, estimation methods for nonlinear nonGaussian time series models that exploit resampling schemes will be explained.
Computational Statistics Model ／ Junji Nakano / Professor
Statistical models which use computer intensively are explained. Techniques of data mining and interactive visual data handling are mainly discussed.
Statistical Computing Ⅰ ／ Junji Nakano / Professor
Technologies for building advanced statistical computation systems are discussed. We focus on distributed computing over Internet, userfriendly parallel computation, and interactive data visualization.
Control Theory Ⅰ ／ Yoshihiko Miyasato / Professor
Control Theory Ⅰ provides basic preliminaries in the field of control theory, such as state space representation, controllability and observability, canonical form, state feedback and optimal LQ control, state observer and Kalman filter, and servo control based on internal model principle.
Control Theory Ⅱ ／ Yoshihiko Miyasato / Professor
Control Theory Ⅱ focuses on several recent topics in the field of advanced control theory, such as adaptive control (model reference adaptive control and self tuning controller), nonlinear control (exact linearization and backstepping), robust control (robust analysis and Hinfinity control), and related system identification methodology (subspace method, recursive estimation method, and closedloop identification). Control Theory Ⅱ is based on preceding Control Theory Ⅰ.
Applied Probability Ⅰ ／ Atsushi Yoshimoto / Professor
Through this course, applications of a counting process, queueing theory and other stochastic processes are emphasized for prediction of renewable resources supply prediction and control.
Applied Probability Ⅱ ／ Atsushi Yoshimoto / Professor
Through this course, application of option theory and mathematical economics are studies for risk management of renewable resources.
Stochastic systems Ⅰ ／ Shinsuke Koyama / Associate Professor
This course provides an elementary introduction of stochastic analysis and its applications.
Stochastic Modeling Ⅱ ／ Shinsuke Koyama / Associate Professor
This course provides advanced topics on stochastic modeling and analysis.
Basic theory of Point Processes ／ Zhuang, Jiancang / Associate Professor
This course gives an introduction to the probability theory of point processes, including the concepts of random measures, Janossy density, Janossy measure, Campbell measure, moment measure, conditional intensity, Papangelou intensity, Palm intensity, etc..
Statistical Inferences for Point Processes ／ Zhuang, Jiancang / Associate Professor
This course is on the techniques related to statistical inferences for random events in time and/or geographical space. In details, we focus on the issues of model construction, information recognition, model diagnostics, model selection, simulation, forecasting, forecast evaluation, etc..
Digital Signal Processing ／ Yumi Takizawa / Associate Professor
This lecture provides basic methods of treatment on signals and transfer functions based on ztransformation with practical design skill for digital system including prediction filters.
Communication and Information Systems ／ Yumi Takizawa / Associate Professor
This lecture provides basic study of information theory by C.E.Shannon referring to contitative expression of information, fundamental characteristics and coding methods for information source and communication channel.
Statistical Computing Ⅱ ／ Shin'ya Nakano / Associate Professor
Statistical computing using parallel computing is the subject of this course. In particular, the following subjects will be discussed: problems which requires huge matrices, the particle filter using a parallel computer, and implementation of the ensemble Kalman filter on a parallel computer.
Spatiotemporal Data Analysis ／ Shin'ya Nakano / Associate Professor
Statistical modelling and analysis of spatiotemporal data and their applications are covered in this course. In particular, the subjects which are applied for geoscience data analysis such as data assimilation will mainly be discussed.
To be determined ／ Hideitsu Hino / Associate Professor
To be determined ／ Hideitsu Hino / Associate Professor
Information Security Ⅰ ／ Kazuhiro Minami / Associate Professor
This course covers privacypreserving data mining techniques for analyzing big data with sensitive information safely.
Information Security Ⅱ ／ Kazuhiro Minami / Associate Professor
This course covers anonymization and differential privacy techniques for publishing datasets for secondary use safely.
Functional Information Theory ／ Fumikazu Miwakeichi / Associate Professor
Here, we study functional aspects of information. By employing statistical approach, quantitative and qualitative handling of the issue is enabled.
Inductive Information Theory ／ Fumikazu Miwakeichi / Associate Professor
Too much information is not information. This paradox is resolved by inductive reasoning.
Statistical Modeling Research Ⅰ〜Ⅴ ／ All the teaching staff in the field of Statistical Modeling
This is a general course on statistical science consisting of seminars, special lectures and drills. Special emphasis is given to statistical modeling and modeling methodologies.
Special Topics in Statistical Modeling Ⅰ ／
This course focuses on classical inference and the linear model that is the foundation of statistical modeling.
Special Topics in Statistical Modeling Ⅱ ／
This course is the second course of "Special Topics in Statistical Modeling I", focusing on Bayesian methods for solving unidentifiable problems appearing in the analysis of sequential or nonsequential data.
Service Sciences Ⅰ ／
Service sector is quickly becoming the largest segment of the modern industry. This class reviews the current private and publicservice businesses and discusses the discipline called "Services Science," which attempts applying mathematical models both to improve productivity and to increase the values of the services.
Service Sciences Ⅱ ／
Based on the discussions provided in "Service Science I," this class conducts case studies of services science by working with companies and government and nongovernment organizations.

To be determined ／ Yoichi M. ITO / Professor
To be determined ／ Yoichi M. ITO / Professor
Biostatistics ／ Koji Kanefuji / Professor
We study the application of statistical methods to problems concerning the medical and biological sciences.
Environmental Statistics ／ Koji Kanefuji / Professor
We study the application of statistical methods to problems concerning the environment.
Communication Information Processing ／ Tomoko Matsui / Professor
Spoken language is a crucial component of human communication. In this course, we study algorithms to process and analyze the information contained in this medium.
Multimedia Information Processing ／ Tomoko Matsui / Professor
The digital age has fostered the broadcasting of an ever increasing quantity of complex multimedia documents, be it through the internet or more versatile electronic channels. These evolutions have called for new tools and technologies to classify and analyze multimedia contents. We study in this course algorithms which are useful for these tasks.
Financial Statistics Ⅰ ／ Satoshi Yamashita / Professor
The course provides students with necessary knowledge and techniques in control and evaluation of credit financial risks. Also, the course introduces leadingedge technology in banks and other financial agencies.
Financial Statistics Ⅱ ／ Satoshi Yamashita / Professor
The course provides students with necessary case studies and techniques in control and evaluation of financial market risks. Also, the course introduces investment statistical models in pension funds and other financial agencies.
Biological System Analysis Ⅰ ／ Ryo Yoshida / Professor
This course covers a range of statistical methods in bioinformatics and materials informatics. Starting from a brief overview of machine learning, Bayesian inference, and R language programming, the essence of statistical modeling and inference is illustrated through practical applications in DNA sequence analysis, bioimage informatics, material design problems, and so on.
Biological System Analysis Ⅱ ／ Ryo Yoshida / Professor
As the second course of "Biological System Analysis Ⅰ", this course conducts studies of more practical and advanced machine learning techniques in bioinformatics and materials informatics.
On CrossNational Comparability of National Character Ⅰ ／ Ryozo Yoshino / Professor
Lecture on the paradigm called Cultural Linkage Analysis (CLA) of the crossnational comparability of social survey data.
On CrossNational Comparability of National Character Ⅱ ／ Ryozo Yoshino / Professor
Lecture on the paradigm called Cultural Manifold Analysis (CULMAN) for the analyses of social survey data. This presents the padradimn called CULMAN for the studies charactercrossnational comparability of social survey data on national character, gathered under different languages and different statistical random sampling.
Genomic Data Analysis Ⅰ ／ Jun Adachi / Associate Professor
Genomic data analysis using inferring phylogenies from DNA sequences and their applications to evolutionary problems.
Genomic Data Analysis Ⅱ ／ Jun Adachi / Associate Professor
Analysis of mechanisms of genome evolution and comparison of the genome structure.
Spatial Statistics ／ Kenichiro Shimatani / Associate Professor
Lectures are given on Spatial Statistical modeling and statistical inferences about spatial data. Basic statistical analytic techniques for sampled data from a continuously chaging variable, lattice data (e.g. data are given for each prefecture or city), point patterns (configuration), and circular data are explained.
Stochastic Geometry ／ Kenichiro Shimatani / Associate Professor
Offers a series of lectures on statistical models of spatial events, such as the models of "Stochastic Geometry" (spatial tessellation, random packing and so on) together with their mathematical foundation and application. Exercises related to problems in "Stochastic Geometry" are also given.
Special Topics in Biostatistics ／ Hisashi Noma / Associate Professor
This course deals with recent relevant topics on biostatistics, especially, (i) Biostatistical methodology on clinical and epidemiologic studies, (ii) Designs and analyses of clinical trials, (iii) Evidence synthesis methods, and (iv) statistical analyses of largescale genomic data.
Applied Statistics Ⅰ ／ Hisashi Noma / Associate Professor
This course deals with practical data analysis methods widely applied in scientific investigation and research, involving practices using statistical software R.
To be determined ／ Yoosung Park / Associate Professor
To be determined ／ Yoosung Park / Associate Professor
Statistics in Medicine Ⅰ ／ Ikuko Funatogawa / Associate Professor
The aim of this course is to study the statistics in medicine and public health focusing on statistical models such as linear mixed effects models in longitudinal data analysis.
Statistics in Medicine Ⅱ ／ Ikuko Funatogawa / Associate Professor
The aim of this course is to study the statistics in medicine and public health focusing on the design such as randomization and also statistics in actual health problems such as obesity and smoking.
Topics in Sampling Theory Ⅰ ／ Tadahiko Maeda / Associate Professor
This course deals with various research designs and statistical inference based on data collected under these designs, with special emphasis on sampling theory. It aims to enhance the students' understanding of the importance of the design stage of reserach process.
Topics in Social Research ／ Tadahiko Maeda / Associate Professor
This course deals with statistical approaches to various problems in the administration of social surveys, such as questionnaire design, nonsampling errors, survey mode comparison, and so on. Taking a few domestic and overseas surveys as examples, we will discuss various sources of errors in those surveys.
Data Science Research Ⅰ〜Ⅴ ／ All the teaching staff in the field of Data Science
This is a general course on statistical science consisting of seminars, special lectures and drills. Special emphasis is given to methodologies on survey and sampling, data analysis and statistical software.
Statistical causal inference Ⅰ ／
This course focuses on statistical causal inference based on graphical models. The application of statistical causal inference to practical science is also discussed.
Statistical causal inference Ⅱ ／
This course focuses on statistical causal inference based on potential outcome frameworks. The application of statistical causal inference to practical science and the difference between graphbased causal inference and the potential outcome framework are also discussed.
Survey Research Data Analysis ／
This course introduces various scaling techniques of categorical data, including quantification methods and homogeneity analysis.
Special Topics in Survey Data Analysis Ⅱ ／
This course introduces various sampling techniques and analytical methods of complex sample survey data. Exercises using statistical software package are also carried out.
Topics in Sampling Theory Ⅱ ／
This course explains how to plan and conduct repeated social surveys and investigates methods of analyzing data obtained from repeated surveys, such as cohort analysis that separates the age, period, and cohort effects on social change.
Special Topics in Survey Data Analysis Ⅰ ／
Examines exploratory data analysis methods for data obtained from surveys in the fields of social, medical and health sciences, including methods of numerical computation and numerical experiments necessary for data analysis.
Applied Time Series Analysis Ⅰ ／
This cource introduces linear and nonlinear time series analysis, parameters identification, basic theory of prediction and its applications.
Applied Time Series Analysis Ⅱ ／
This cource introduces several time series modeling and estimations (Threshold model, Exponential AR model, RBFneural network, state space model, dynamical system model, stochastic differential equation model, etc.). Their applications to dynamics of inverse problems in neuroscience and physical sciences are also introduced.
Statistical Computing Ⅲ ／
Study mathematical basics of coding theory and some basic features of codes.
Statistical Computing Ⅳ ／
Based on understanding of "Statistical Computing Ⅲ", study recent researches on soft decision decoding.

Special Topics in Signal Processing Ⅰ ／ Shiro Ikeda / Professor
This course introduces the basic theory of signal processing including the Principal Component Analysis and Independent Component Analysis.
Special Topics in Signal Processing Ⅱ ／ Shiro Ikeda / Professor
This course introduces how to apply signal processing methods to real data analysis including speech signals and biological data.
Systems Optimization Ⅰ ／ Satoshi Ito / Professor
This course is intended to serve an introduction to systems design and analysis, and focuses on the theoretical aspects of convex optimization based on convex analysis, duality theory and numerical linear algebra.
Systems Optimization Ⅱ ／ Satoshi Ito / Professor
We will discuss several specific topics in continuous optimization, including hierarchical optimization, robust optimization and infinitedimensional optimization, with some applications in control, signal processing, machine learning and other systems design.
Statistical Learning Theory Ⅰ ／ Shinto Eguchi / Professor
The theory and applications are lectured through examples of boosting method, support vector machine, kernel space method, Bayesian network.
Information Geometry ／ Shinto Eguchi / Professor
A framework on an information space is introduced for deeper understanding on uncertainty from a geometric viewpoint.
Multivariate Statistical Inference Ⅰ ／ Satoshi Kuriki / Professor
One of the topics below will be chosen: (1) Contingency table and graphical model (2) Distribution theory and statistical inference (3) Differential and integral geometric approach to statistics (4) Algebraic statistics.
Multivariate Statistical Inference Ⅱ ／ Satoshi Kuriki / Professor
Seminar on a particular topic related to multivariate analysis, categorical data analysis, graphical models, asymptotic inference, distribution theory, random field, algebraic statistics, and relevant mathematics such as differential geometry, convex analysis, combinatorics and measure theory.
To be determined ／ Yoshiyuki Ninomiya / Professor
To be determined ／ Yoshiyuki Ninomiya / Professor
Statistical Machine Learning ／ Kenji Fukumizu / Professor
This course discusses machine learning methods for analyzing large and high dimensional data.
Statistical Learning Theory Ⅱ ／ Kenji Fukumizu / Professor
This course discusses theory and methodology for automatic knowledge acquisition from data, based on mathematical methods such as probability, functional analysis, geometry, and discrete mathematics.
Theory of Statistical Inference ／ Hironori Fujisawa / Professor
Robust inference against outlier, including robust estimation, test and model selection.
Special Topics in Data Analysis Ⅰ ／ Hironori Fujisawa / Professor
Statistical methods for analysis of data, especially for analysis of medical data.
Regression Analysis ／ Shogo Kato / Associate Professor
This course deals with some topics on the theory of regression analysis, especially, generalized linear models. In addition, applications of the theory of regression analysis to real problems are discussed.
Distribution Theory ／ Shogo Kato / Associate Professor
This course provides an overview of the theory of probability distributions which are commonly used in statistics. Statistical models related to these distributions are also discussed.
To be determined ／ Takaaki Shimura / Associate Professor
To be determined ／ Takaaki Shimura / Associate Professor
Topics of Statistical Inference Ⅰ ／ Masayuki Henmi / Associate Professor
The aim of this course is to study the theory and application of statistical inference based on semiparametric models with infinitedimensional nuisance parameters.
Topics of Statistical Inference Ⅱ ／ Masayuki Henmi / Associate Professor
The aim of this course is to study statistical methods for data that is sampled with bias from a population of interest, focusing on methods for statistical analysis with missing data.
Special Topics in Data Analysis Ⅱ ／ Shuhei Mano / Associate Professor
This course discusses modeling of probability measures and methods and practices in data analysis.
Stochastic Models ／ Shuhei Mano / Associate Professor
This course is on random combinatorial models, especially partition structures, and the statistical inferences.
Statistical Natural Language Processing ／ Daichi Mochihashi / Associate Professor
We discuss basic statistical methods for natural language or similar discrete data, and related problems for inference and learning.
Bayesian Modeling and Sequential Monte Carlo Methods ／ Daichi Mochihashi / Associate Professor
Advanced modeling and scientific computing to combine a wide variety of information sources within a framework of Bayesian approach. A special focus is laid on the statistical modeling for timeseries analysis in geoscience, marketing, and bioinformatics.
Mathematical Analysis and Statistical Inference Ⅰ〜Ⅴ ／ All the teaching staff in the field of Mathematical Analysis and Statistical Inference
This is a general course on statistical science consisting of seminars, special lectures and drills. Special emphasis is given to mathematical/inferential/computational aspects of statistical science.
Optimization Algorithm Ⅰ ／
Mathematical optimization problems are formulated to find the best solution under some constraints. To solve an optimization problem, we need to choose a suitable algorithm that exploits the essential features of its formulation. The goal of this course is to acquire the ability to solve basic linear, nonlinear and integer programming.
Optimization Algorithm Ⅱ ／
This course discusses practical algorithms (relaxation, approximation and randomized algorithms) for optimization problems that are inherently too difficult to solve exactly by traditional means (e.g. standard optimization methods discussed in the class Optimization Algorithm Ⅰ). The goal of this course is to acquire the knowledge about these practical algorithms through reading textbooks and literature.

Statistical Science Study Ⅰ〜Ⅴ ／ All the teaching staff of Department of Statistical Science
This is a general research course of statistical science. Students are requested to present progress of their research by giving seminars and talks.
Statistical Science Ⅰ〜Ⅴ ／ All the teaching staff of Department of Statistical Science
This is a general course on statistical science consisting of seminars and special lectures. Emphasis is laid on important advanced topics in statistical science.
Statistical Mathematics Seminar Ⅰ〜Ⅴ ／ All the teaching staff of Department of Statistical Science
This is a general course of statistical science. Students are requested to attend the statistical mathematics seminar held at the institute of statistical mathematics to learn various recent developments in statistical science.
