統計関連学会連合大会プレナリーセッション

第 2 回 赤池メモリアルレクチャー【日 時】 2018 年 9 月 10 日（月）10:30～12:00【会 場】 中央大学・後楽園キャンパス 5 号館 5 階 5534 教室（A 会場）【主 催】 統計関連学会連合大会組織委員会、 統計数理研究所【司 会】 栗原 考次（統計関連学会連合大会組織委員長）【座 長】 樋口 知之（統計数理研究所長）【概 要】 「赤池メモリアルレクチャー賞」は、2016 年 5 月に統計数理研究所と日本統計学会により共同で設立されました。統計科学の分野において多大な功績を残し、その発展に大きな影響を及ぼした故赤池弘次博士の功績を記念したものです。
今年度、第 2 回受賞者がデューク大学の Mike West 教授に決定し、受賞式及び記念
講演を 2018 年度統計関連学会連合大会のプレナリーセッションで行います。
【プログラム】
10:30～10:40
赤池メモリアルレクチャー賞の説明および授賞式
樋口 知之 (統計数理研究所長)
10:40～11:40
赤池メモリアルレクチャー"Bayesian Forecasting of Multivariate Time Series: Model Scalability,
Structure Uncertainty and Decisions"
Mike West 教授 (米国デューク大学 特別栄誉教授)
11:40～12:00
二人の若手研究者による討論および West 教授の答弁
中島 上智 博士 (国際決済銀行）
Assist. Prof. Christopher D. Glynn (ニューハンプシャー大学)

【受賞者】
Mike West 教授
略歴：1978 年
イギリス ノッティンガム大学数学部 卒業 ＊一等学位
1982 年
博士（数学（統計学））取得（イギリス ノッティンガム大学）
1981-1988 年 イギリス ウォーリック大学 統計学科講師
1984 年
アメリカ ハーバード大学客員教授
1987 年
アメリカ パデュー大学客員教授
1988-2003 年 イギリス ウォーリック大学 統計学部准研究員
1994 年
アメリカ 国立統計科学研究所（NISS）上席・大学研究員
1988-1992 年 アメリカ デューク大学 統計科学・意思決定科学研究所准教授
1990-2002 年 同 所長
1992-2007 年 同 教授
1999 年-現在 デューク大学 特別栄誉教授（Distinguished Professor）
2007 年-現在 デューク大学 統計学科教授【討論者】
中島 上智 博士
Dr. Jouchi Nakajima
シニアエコノミスト Senior Economist
金融経済局
Monetary and Economic Department
国際決済銀行Bank for International Settlements
Dr. Christopher Glynn
Assistant Professor of Decision Sciences
Peter T. Paul College of Business and Economics
University of New Hampshire＜本件に関するお問い合わせ先＞
大学共同利用機関法人 情報・システム研究機構 統計数理研究所
運営企画本部企画室 URA ステーション
TEL：050-5533-8580 E-mail：ask-ura@ism.ac.jp〒190-8562 東京都立川市緑町 10-3

Introduction of
the Akaike Memorial Lecture
Award
Prof. Tomoyuki Higuchi
Director‐General,
The Institute of Statistical Mathematics (ISM)

1

Hirotugu Akaike（1927－2009）

Research Organization ofInformation and Systems*This slide was made by Dr. Genshiro Kitagawa, Ex. President of the Research
Organization of Information and Systems (ROIS)

Brief History
1952

Graduated from Math. Dept., Tokyo University Researcherof the Institute of Statistical Mathematics

1962
1973
1985
1986
1988

Head of 2nd Section, 1st Division
Director of 5th Division
Director of Dept. of Prediction and Control
Director-General of ISM (-1994)
Member of Science Council of Japan (- 1991)
Chair of Dept. of Statistical Science, Graduate University of
Advanced Study
Prof. Emeritus, ISM
Prof. Emeritus, Graduate Univ. for Advance Study

1994

Research Organization ofInformation and Systems*This slide was made by Dr. Genshiro Kitagawa, Ex. President of the Research
Organization of Information and Systems (ROIS)

Prizes1972 Ishikawa Prize（Establishment of statistical analysis and control method for dynamic systems）

1980

Okochi Prize（Research and realization of optimal steam temperature control of thermal electric plant）

1989 Asahi Prize（Research on statistics, in particular theory and applications of AIC）

The Purple Ribbon Medal（Statistics, in particular time series analysis and its applications）

1996 The 1st Japan Statistical Society Prize（Contributions to statistical theory and its applications）

2000 The Order of the Sacred Treasure
2006 Kyoto Prize（ Major contribution to statistical science and modeling with the development of AIC）

Fellow of ASA, RSS, IMS, IEEE, JSS
Research Organization ofInformation and Systems*This slide was made by Dr. Genshiro Kitagawa, Ex. President of the Research
Organization of Information and Systems (ROIS)

Laureate of 22nd Kyoto Prize"Major contribution to statistical scienceand modeling with the development of
the Akaike Information Criterion (AIC)”

Research Organization ofInformation and Systems*This slide was made by Dr. Genshiro Kitagawa, Ex. President of the Research
Organization of Information and Systems (ROIS)

Celebration for the 90th-Birthday of Dr. Hirotugu Akaike

If you've ever conducted a statistical analysis, you might’ve spent hours thinking about which variables to include and the impact each would have on theoutcome. But to ensure the model itself is accurate, shouldn’t someone measure the measurers?
In the early 1950s, a young Japanese scientist named Hirotugu Akaike asked this simple but crucial question. More than two decades of research later, he
presented the answer as a simple equation known as the Akaike Information Criterion. With AIC, analysts select a model from a set of options by measuring how
close the results are to the (hypothetical) truth.

For Dr. Akaike, experience was core to creativity. To get ‘a direct feel of random vibrations,’ for example, he bought ascooter and rode it around Mount Fuji. This first-hand experience helped him differentiate between the vibrations of riding on normal and heavily-trucked roads.
Today’s Doodle portrays Dr. Akaike against a Google-inspired approximation of functions, parameters, and their respective curves.
Below are a few initial conceptualizations of the Doodle.
https://www.google.com/doodles/hirotugu-akaikes-90th-birthday

6/27

For more information of
Dr. Hirotugu Akaike,please visit our website.

2000/June/9

2004/Nov/20

http://www.ism.ac.jp/akaikememorial/index‐e.html
7Japan Statistical Society
June 25, 2018

Announcement of the Awardee of
the Second Akaike Memorial Lecture Award◆ Overview:
In May of 2016, the Institute of Statistical Mathematics (ISM) and the Japan Statistical Society (JSS) havejointly created the Akaike Memorial Lecture Award to celebrate the outstanding achievements of the late Dr.
Hirotugu Akaike. Dr. Akaike greatly influenced a wide range of research by proposing the Akaike
Information Criterion (AIC) and establishing a novel paradigm for statistical modeling, which was
distinguished by its predictive point of view, and was totally distinct from traditional statistical theory. The
Akaike Memorial Lecture Award aims to encourage the education of talented young researchers byrecognizing researchers who have achieved outstanding accomplishments that contribute to the field ofstatistical sciences.
With great pleasure, we announce that the awardee of the Second Akaike Memorial Lecture Award isProfessor Mike West of Duke University in USA. Professor West’s contributions to Bayesian statistics
include seminal work in dynamic modeling, and the implementation of nonparametric models that paved theway to practical data analyses via the first realization of large-scale simulation-based methods. Professor
West himself has also actively worked at the frontiers of various research fields to which Bayesian statistics
can be applied and contributed to the creation of data-driven sciences. For example, he established a new
approach for biomarker discovery using gene expression data, thus creating a novel trend in omics biology
based on data analysis. The award ceremony and memorial lecture will be held during the plenary session of
the Japanese Joint Statistical Meeting 2018, which will take place at the Korakuen Campus of Chuo
University on September 10, 2018.

Reason for Selection:
Professor Mike West is a great pioneer in the field of Bayesian statistics. He receives the Second Akaike
Memorial Lecture Award for his wide-ranging and outstanding research accomplishments in both theoreticaland practical aspects of this field.
Professor West has made significant contributions to the development of statistical sciences, including
dynamic modeling, space–time data analysis, sparse modeling, Bayesian computation, and non-parametric
Bayesian analysis. The applied fields to which Prof. West has contributed include finance, macroeconomics,
climatology, and biology. His paper on Dirichlet Process mixture models, published in 1995, offered an easy1/5

Japan Statistical Society
to-implement and efficient algorithm for the model. Previously, even though its usefulness had been
understood theoretically, this model had not been used in practice due to difficulties in its computation.
Professor West’s algorithm represented a major contribution to the practical realization of the non-parametric
Bayesian analysis. The magnitude of this influence is illustrated by the fact that the paper has been cited morethan 2000 times [1]. In addition, in 2001 and 2006, Professor West published important papers pertaining tocancer prognosis using gene expression data, conducted jointly with leading molecular geneticists. Thesepapers identified patterns of cell signaling pathway deregulation by combining signature-based predictions
and developed predictive models for identifying types of breast cancer [2,3]. These outcomes led to theoccurrence of a mega-trend in omics biology: biomarker discovery through gene expression profiling.
For these reasons, the nominating committee is proud to select Professor West as the awardee of the Second
Akaike Memorial Lecture Award.

◆ About the Second Awardee: Professor Mike West
Research Achievements
Professor Mike West is one of the pioneers who have led the field of the Bayesian statistics. In particular,
Professor West’s research on Bayesian modeling of dynamic processes, high-dimensional data and largescale complex systems has significantly influenced the development of the theory and practice of statistics.
Professor West’s broad activities range from statistical theory, including time series, high-dimensional space–time data analysis and sparse modeling, to application, including decision-making theory for financial
time-series data analysis, structure modeling, analysis of high-frequency financial time series data,
macroeconometrics, dynamic network analysis, image recognition, system biology, and analysis of air
environment monitoring data.
After obtaining his Ph.D. in Mathematics at the University of Nottingham, UK, Professor West has
published nearly 200 papers in numerous fields, ranging from pure statistical theory to applied research infields including business, economy and finance, signal processing, climatology, public health sciences,
genome sciences, immunology, neurophysiology, and systems biology.
Professor West has devoted himself passionately to education in statistics, and has made outstanding
accomplishments in education. Over the course of his career, he has instructed young statisticians fromindustry, government, and academia around the world, and mentored more than 55 talented doctoral students
and post-doctoral researchers.
Additionally, Professor West was awarded the Mitchell Prize (three times, in 1994, 1997, and 2012); theAmerican Statistical Association Outstanding Statistical Application Paper Award (1997); the American
Statistical Association NC Chapter Award (2014); and the Zellner Medal from the International Society for
Bayesian Analysis (2014).
2/5

Japan Statistical Society[1] Escobar, M.D., West, M.(1995). Bayesian density estimation and inference using mixtures. Journal of theAmerican Statistical Association 90:577-588.
[2] Bild, A.H., Yao, G., Chang, J.T., Wang, Q., Potti, A., Chasse, D., Joshi, M., Harpole, D., Lancaster, J.M.,
Berchuck, A., J.A. Olson, J.R.M., Dressman, H.K., West, M., Nevins, J.R.(2006). Oncogenic pathway
signatures in human cancers as a guide to targeted therapies. Nature 439:353-357.
[3] West, M., Blanchette, C., Dressman, H.K., Huang, E.S., Ishida, S., R. Spang, H.Z., Marks, J.R., Nevins,
J.R. (2001). Predicting the clinical status of human breast cancer utilizing gene expression profiles.
Proceedings of the National Academy of Sciences 98:11462-11467.
Current Position
The Duke University distinguished Arts & Sciences Professor of Statistics and Decision Sciences,in the Department of Statistical Science, Duke University
Biography
Date of birth:

October 30th, 1956 (current age, 61)

Education:
1978 B.Sc. Mathematics, First Class Honors, Department of Mathematics, University of Nottingham, UK
1982 Ph.D. in Mathematics (Statistics), University of Nottingham, UK
Professional Summary
1981–1988

Lecturer at Department of Statistics of University of Warwick, UK

1984

Visiting Professor at Harvard University, USA

1987

Visiting Professor at Purdue University, USA

1988-2003

Associate Fellow at Department of Statistics of University of Warwick

1994

Senior & University Fellow at the National Institute of Statistical Sciences (NISS), USA

1988–1992

Associate Professor at the Institute of Statistics and Decision Sciences (ISDS) of
Duke University, USA

1990–2002

Director at ISDS

1992–2007

Professor at ISDS

1999–present

Distinguished Professor at Duke University
Professor in the Division of Statistics of Duke University

2007–present

Professor in the Department of Statistical Science, Duke University◆ Overview of the Akaike Memorial Lecture Award
The Akaike Memorial Lecture Award was inaugurated in 2014 under the joint sponsorship of ISM and JSS.
The Award was named after the late Dr. Hirotugu Akaike (*1), who left a wide-reaching and influential legacy
3/5Japan Statistical Societyof research in the statistical sciences. Along with the Award, the ISM and JSS organize a memorial lectureby the awardee, offering opportunities for exchange among statistical researchers from within and outside
Japan as well as inspiration to young and talented researchers. Thus, the Award contributes to further
advances in this field.
Every 2 years, one awardee is selected from among those individuals who are, like the late Dr. Akaike,ahead of their times, exercising an international influence over a wide range of fields in the statistical sciences(including mathematical sciences and mathematical engineering, such as control and optimization) andrelated applied fields. The awardee receives a ¥100,000 honorarium, an award plaque, and travel expenses.
For educational purposes, the Memorial Lecture will be followed by time for question and discussion
involving the Awardee and selected students and young researchers. The contents of the lecture, including theaccompanying discussion, will be published as an invited article in the Annals of the Institute of Statistical
Mathematics (AISM).

◆ The Second Akaike Memorial Lecture
The Second Akaike Memorial Lecture will be held during the plenary session of the 2018 Japanese
Joint Statistical Meeting, which will be co-organized by the Institute of Statistical Mathematics and theOrganizing Committee of the Japanese Joint Statistical Meeting 2018, commissioned by The Japan
Statistical Society.
Lecturer:

Professor Mike West (Duke University)

Title:

Bayesian Forecasting of Multivariate Time Series:
Model Scalability, Structure Uncertainty and Decisions

Discussants:

Dr. Jouchi Nakajima (Bank for International Settlements)
Dr. Christopher D. Glynn (University of New Hampshire)

Date/Time:

September 10, 2018 (a.m.)

Venue:

Building No. 5, Room 5534, Korakuen Campus, Chuo University(1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
http://global.chuo-u.ac.jp/english/siteinfo/visit/korakuen/ )Note: Detailed information will be uploaded on the following websites:
Institute of Statistical Mathematics (http://www.ism.ac.jp/index_e.html)
Japanese Joint Statistical Meeting 2018 (http://www.jfssa.jp/taikai/2018/english/index.html)
Japan Statistical Society (http://www.jss.gr.jp/en/).

4/5

Japan Statistical Society(*1) Biography of Dr. Hirotugu Akaike
Born on November 5, 1927 in Shizuoka Prefecture, Japan, Hirotugu Akaike graduated from the Imperial
Naval Academy, the First Higher School, and the Department of Mathematics, and the Faculty of Science,
The University of Tokyo. He joined the Institute of Statistical Mathematics in 1952.
In the 1960s, Dr. Akaike led the field of time series analysis through his research and development ofspectral analysis techniques, multivariate time series models, statistical control methods, and TIMSAC (TimeSeries Analysis and Control), a software package designed for time-series analysis. In the 1970s, Dr. Akaike
proposed the Akaike Information Criterion (AIC). Thus, he established a new paradigm of statisticalmodeling, which was characterized by a predictive point of view, and therefore completely distinct fromtraditional statistical theory. His accomplishments have greatly influenced research in a variety of fields. In
1980s, Dr. Akaike advanced his research to realize the practical application of Bayesian modeling. His
research played a pioneering role in the development of new information processing systems that could meetthe demands of the era of large-scale information. His research results were held in the highest esteem by hiscolleagues and earned him many prizes, including the Medal of Honor (Purple Ribbon), the Second Class
Order of the Sacred Treasure, and the Kyoto Prize. His work continues to be cited today.
Dr. Akaike took the position of Director-General of the ISM in 1986. While overseeing the operation of
the Institute, he also took part in establishing and teaching in the Statistical Studies program at the Graduate
University for Advanced Studies. His term as Director-General ended in 1994. At that time, he was appointed
Professor Emeritus at the Graduate University for Advanced Studies, but never lost his passion for research;rather than resting on his well-deserved laurels, he continued his work, publishing studies of topics as diverse
as Bayesian models and the golf swing. He also served as the 19th president of the JSS from January 1989
to December 1990. He passed away in Ibaraki Prefecture, Japan on August 4, 2009 (age 81).
On November 5, 2017, a celebration of Dr. Hirotugu Akaike’s 90th Birthday appeared on the Google
Doodle in 16 countries and regions around the world.

Memorial Website for Late Dr. Akaike: Hirotugu Akaike Memorial Website
http://www.ism.ac.jp/akaikememorial/index-e.html------------------------------

Press Contact:
URA Station in ISM
E-mail: ask-ura@ism.ac.jp
TEL: +81-50-5533-8580

5/5

Akaike Memorial Lecture 2018

Bayesian Forecasting of Multivariate Time Series:
Model Scalability, Structure Uncertainty and Decisions
Mike WestDuke University

Akaike Memorial Lecture : Some of today’s themes

•
•
•
•
•

Bayesian state-space time series
Prediction and decisions
Model structure uncertainty
Computational feasibility
Applied motivations

Hirotugu Akaike

Institute of Statistical Mathematics
State-space modelling- innovators and leadersDynamic data: Contexts- Challenges Flexible multivariate models
Computationally feasible
Scale up in•

Increasingly large-scale:oooHigh-dimensional time series
Dynamic networks
Large-scale hierarchical systems•

Sequential analysis, forecasting, decisions:
o
o
o
o
o

Financial portfolios
Multi-step macroeconomics
Monitoring networks- change/anomaly detection
Large-scale commercial sales forecasting
Business/corporate financial flows, investment decisions

Concept: Decouple/recouple

DECOUPLE - univariate time seriesoooCustomisable series-specific models
State-space models: analytic tractability
Sequential analysis: fast? parallelisation?

RECOUPLE - Critical cross-series / multivariate structureo Sensitive dependence / “co-volatility” modellingo Sensitive online monitoring, adaptation to changeo Coherent joint forecast distributions, resulting decisions

Decouple/recouple : Example 1 - Multivariate volatility
Financial time series: FX, commodities, stocks ..
Multi-step forecast and portfolio goals- daily/weekly: sequential, repeat

Critical cross-series / multivariate structureo Sensitive dependence / “co-volatility” modelling ?

Existing models
o Wishart or Inverse Wishart processes, MGARCH, …
o (In)flexibility?
o Scalable – theoretically? computationally?

Decoupled, parallel univariate dynamic models
Cross-series: directed & sparse*graphical* structure – lagged

FX – daily price $US

Idiosyncratic: volatility¥$Au£

Customised predictors− external/independent factors− lagged y – sparse set
e.g.

€
KrNo

¥ “info”

Contemporaneous coupling
Cross-series: simultaneousgraphical structure

¥

?

e.g.

$Au
£

€

Sparse sets
simultaneous parents

KrNo

Simultaneous dynamic models
dynamic regressions & precision/volatility

diag

SparseNotation: extend

to row j and pad with 0 entries

Non-zeros in .

Dynamic graphical model structure induced

zero precision:
conditional
independence
Non-zeros in .

Sparse volatility matrix model
Non-zeros in .

Simultaneous dynamic linear/state-space modelMultiple univariate models-“decoupled”
in parallel

Residual volatilities:
Tractable Markov dynamics

Parallel states:
Linear, Gaussian, Markov
state evolution models

Special examples: Dynamic dependency network models•

triangular form: by choice or chance•Sparse Cholesky-style precision (… popular)- upper or lower triangular- same story: reorder

Order of series relevant

Sparse, upper triangularNon-zeros in .

DDNMs
Decoupled univariate series: parallel, analytic filtering & forecasting
Recoupling:simulate synthetic futures & combine- exploits triangular “hierarchy”

Finance:Zhao, Xie & West, ASMBI 2016

Jouchi Nakajima
BIS BaselDynamic threshold DDNMs

Finance:

J Fin Econ 2013Intl J Forecasting 2014

Macroeconomics:

J Bus & Econ Stats 2013

Neuroscience:

Dig Sig Proc 2015
BJPS 2017

DDNMs- Customisable univariate models: decoupled, in parallel- Parental structure: sparse stochastic volatility matrix- On-line sequential learning: Analytic, fast, parallel- Compositional recoupling for coherent inferences & predictions

BUT:
dependent on choice of order of named series j=1:m
Scale-up in dimension?
Choice/specification of order?
400 S&P stock price series?

SGDLMs: Simultaneous graphical dynamic models

Simultaneousparents of j

Joint pdf …
likelihood for states and volatilities:• “almost” decoupled!
• - coupling/coherence: “complicating” determinantNon-zeros in .

Decouple/Recouple: Sequential analysis of SGDLM

decoupledrecoupleRecouple: importance sampling
Decouple: variational Bayes

Decouple/Recouple: (i) Time t-1 predictions
Decoupled parallel models: Conjugate state priors- Evolve : Recouple : Forecast -Synthetic futurestates
¥
$Au
£

€
KrNo

Recouple
Joint predictions
Synthetic futuresDecouple/Recouple: (ii) Time t updating
Joint posterior{ decoupled conjugate forms }=IS weights

Importance sampler

Conjugate
state posteriors
¥
$Au
£

€
KrNo

*
*
*

Simulation-based
full posterior

Decouple ?

Observe**

Decouple/Recouple: Time t decoupling
Joint posterior
decoupled conjugate margins
Variational Bayes (VB)
Decoupling at t:
Match product of conjugate forms with importance sample- minimize Kulback-Leibler (KL) divergence- Entropy measure of accuracy- Relates to effective sample size (ESS) of importance sample

Monitor Entropy & ESS

Evolution to t+1:

Decoupled, analytic

Example: S&P stock price seriesMultiple models x portfolio utilities
comparisons

m=401: 400 stocks & S&P index- daily closing prices
Q1/2002 – Q3/2013
Model (log-difference) returns
Training data : 2002
Test/prediction/portfolio decisions :Q1/2003 – Q3/2013

S&P400 portfolios: Examples of cumulative returns

SGDLM: M1

Wishart: W1

Trading costs adjusted returns
Uniform improvements (Sharpe ratios, etc) over WDLMs

Simultaneous parental set specification?
AIC / *IC / full Bayesian model selection / model averaging ?
Parallel stochastic search over parental sets ?

Multi-step forecasting… butDecisions! Portfolio outcomes (risk, return, …)  !

Goals? Learning/choosing “best” statistical models

Adaptive “refresh” of parental sets
- monitor: candidates to add, remove each t -S&P example – Simultaneous parental set turnoversp(3M)

S&P example – Monitoring decoupling/recoupling
I=5,000 Monte Carlo sample size

[ recoupling ]ESS

Entropy[ decoupling ]Potential to intervene- “signal” of deterioration of ESS:
Increase sample size &/or modify model: parental sets, discounts, etc

St Louis Fed “Financial Markets Stress Index”

3/10
Eurozone crisis
EU/ECB 1st responses

8/07
Subprime loan PR
Northern Rock bailout
10/08
US loans buy-back
NES Act

8/11
US credit rating downgrade
Sharp drops in global markets

SGDLM decoupling/recoupling and computations- Flexible, customisable univariate models- Flexible, sparse stochastic volatility matrix model- No series-order dependence- Scale-up : conceptual & computational- On-line sequential learning: Analytics + cheap simulation + VB
… parallelisable within each time step
- GPU computation- Recouple: CPU for coherence- Decouple/Recouple IS&VB: theory, monitoring per series

Decouple/recouple : Example 2 - Network flow monitoring
Counts/flows between nodes over time−n nodes: m=nxn series− .

Web domain: e-commerce−n ~ 20-200+

− .
−
−
−
−

Sensitive tracking of flow dynamics ?
Characterize “normal” patterns of time variation ?
Change/anomaly detection ?
Fast! sequential analysis ?

DECOUPLE - univariate count time seriesoDynamic generalized linear state-space models

RECOUPLE – direct simulationoo

Map to dynamic random effects/interaction model
Bayesian “model emulation”

Decouple/recouple : Example 3 – Multi-scale forecasting
Consumer sales/supermarkets−

University of Tokyo
Graduate School of Economics
Applied Statistics Workshop
16:30 Tues Aug 18th 2018

m ~ 10,000-100,000s

− .
−
−
−
−

Customize individual models
Forecast 1-14 days ahead, each day
Change/anomaly detection
Link across-series : e.g., seasonality

Many series : “similar patterns”−−

Hierarchical models ? Factor models ?
Not scalable− .
DECOUPLE - univariate count time seriesoDynamic binary & count mixture models

RECOUPLE – direct simulationoo“Common” seasonal factors from external model
Series-specific mapping of common factors

Some Bayesian decouple/recouplers
Zoey Zhao
CitadelLutz Gruber

Amy Xie

QuantCo & U Nebraska

Duke

Jouchi Nakajima
BIS Basel

SGDLMs BA 2016
EcoStat 2017

DDNMs ASMBI 2016

Xi Chen

Kaoru Irie

LinkedInU Tokyo*DDNM*s JBES, IJF, JFI, DPS BJPS
2013 - 17

Lindsay BerryDuke

Network flows JASA 2018

Multi-scale models 2018www.stat.duke.edu/~mw

Akaike Memorial Lecture 2018

Hirotugu Akaike•

Applied motivations: forecasting, monitoring & decisions•• Conceptual innovation : Decouple/recouple
Technical innovation : Context dependent structuring•

Practical, efficient and effective scalability