A Bibliography -- on statistics, physics and all that.


Iba's home page


Akaike,H. (1973)
Information theory and
an extension of the maximum liklihood principle
in 2nd International Symposium on Information Theory
Eds. Petrov,B.N. and Csaki,F
Akademiai Kiado, Budapest, 267-281.

赤池弘次 (1976)
情報量規準AICとは何か
数理科学 No.153, 1976年 3月号(特集・情報量規準), pp.5-11.

Akaike,H. (1977)
On entropy maximization principle
in Application of Statistics
Ed. P.R.Krishnaiah
North Holland, 27-41.

Akaike,H. (1979)
A Bayesian extension of the minimum AIC procedure of autoregressive model fitting
Biometrika 66 53-59.

Akaike,H. (1980)
On the use of predictive likelihood of a Gaussian model
Annals of the Institute of Statistical Mathematics 32 A?? 311-324

Akaike,H.(1980)
Likelihood and Bayes procedure
in Bayesian Statistics,
Eds. Bernardo,J.M, DeGroot,M.H., Lindley,D.V., and Smith,A.F.M.,
University press, ¥ Valencia.

赤池弘次 (1981)
モデルによってデータを測る
数理科学 No.213, 1981年 3月号(特集・統計モデル), pp.7-10.

Akaike,H. (1987)
Factor analysis and AIC
Psychometrika, Vol.‾3, 317-332.

Akaike,H.(1989)
in ベイズ統計学とその応用,
(編)鈴木雪夫 国友直人,
東大出版会.

Aloimonos,Y. (1994)
What I have learned (A reply to Tarr and Black's paper)
CVGIP: Image Understanding, Vol.60, pp.74-85.

Amit,D.J., Gutfreund,H. and Somplinsky,H. (1985)
Spin-glass models of neural networks
Physical Review A 32 1007-1018.

Amit,Y.,Grenander,U. and Piccioni,M(1991)
Structural image restoration through deformable templates
Journal of the American Statistical Association 86 376-387.

Angles d'Auriac, Preissmann,M. and Rammal,R.(1985)
The random field Ising model : Algorithmic complexity and phase transition
Journal de Physique Letters 46 L173-L180.

Bachas,C.(1984)
Computer-intractability of the frustration model of a spin glass
Journal of Physics A 17 L709-L712.

Bak,P. and von Boehm,J.(1980)
Ising Model with solitons, phasons, and ``the devil's staircase''
Physical Review B 21 5297-5307.

Barry,D. and Hartigan,J.A.(1993)
A Bayesian analysis for change point problems
Journal of the American Statistical Association 88 309-319.

Belsley,D.A., Kuh,E. and Welsch,R.E. (1980)
Regression Diagnostics,
Wiley, New York.

Berg,B.A. and Celik,T.(1992a)
New approach to spin-glass simulations
Physical Review Letters 69 2292-2295.

Berg,B.A. and Celik,T.(1992b)
Multicanonical spin glass simulations
International Journal of Modern Physics C 3 1251-1274.

@@ Berg,B.A. and Neuhaus,T.(1991)
Physics Letters B, 267, p.249-?.

Berg,B.A. and Neuhaus,T.(1992)
Multicanonical ensemble: a new approach to simulate first-order phase transitions
Physical Review Letters 68 9-12.

Besag,J.(1974)
Spatial interaction and the statistical analysis of lattice systems
The Journal of the Royal Statistical Society B 36 192-236 (with discussion).

Besag,J.(1986)
On the statistical analysis of dirty pictures
The Journal of the Royal Statistical Society B 48 259-302 (with discussion).

Besag,J.(1989)
Towards Bayesian image analysis
Journal of Applied Statistics 16 395-407.

Besag,J and Green,P.J.(1993)
Spatial statistics and Bayesian computation
Journal of Royal Statistical Society B 55 25-37 (with discussion).

Besag,J., Green,P., Higdon,D. and Mengersen,K. (1995)
Bayesian computation and stochastic systems
Statistical Science 10 3-66 (with discussion).

Besag,J., York,J. and Molli¥'{e},A (1991)
Bayesian image restoration, with two applications in spatial statistics
Annals of the Institute of Statistical Mathematics 43 1-59 (with discussion).

Bialek,W. and Zee,A.(1987)
Statistical mechanics and invariant perception
Physical Review Letters 58 741-744.
@ Bialek,W. and Zee,A. (1988)
Understanding the efficiency of human perception
Physical Review Letters ???

Binder,K.(ed.)(1984)
Applications of the Monte Carlo Method in statistical physics
Topics in current physics vol.36
Springer-Verlag, Berlin.

Binder,K.(ed.)(1986),
Monte Carlo Methods in Statistical Physics (2nd ed.),
Topics in current physics vol.7, Springer-Verlag, Berlin.

Binder,K. (1986@)
Introduction: Theory and ``technical'' aspects of Monte Carlo simulations
in Monte Carlo Methods in statistical physics (2nd ed.)
Topics in current physics vol.7
Ed. Binder,K., ¥ Springer-Verlag, Berlin.

Binder,K.(ed.)(1987),
Applications of the Monte Carlo Method in Statistical Physics (2nd ed.),
Topics in Current Physics vol.36, Springer-Verlag, Berlin.
( 1st ed.(1984), 2nd ed. は未入手).

Binder,K.(ed.) (1992)
The Monte Carlo Method in Condensed Matter Physics
Topics in Applied Physics vol.71
Springer-Verlag, Berlin
(未入手).

Binder,K.(ed.)(1995),
The Monte Carlo Method in Condensed Matter Physics (2nd ed.),
Topics in Applied Physics vol.71, Springer-Verlag, Berlin.

Binder,K. and Heermann,D.W. (1992)
Monte Carlo simulation in statistical physics ¥ An introduction ¥ (2nd ed.)
Springer series in solid-state sciences 80
Springer-Verlag, Berlin.

Binder,K. and Stauffer,D. (1984)
A simple introduction to Monte Carlo simulation
and some specialized topics
in Applications of the Monte Carlo Method in statistical physics
Topics in current physics vol.36
Ed. K. Binder, ¥ Springer-Verlag, Berlin
(2nd ed.(1987)にも載っていると思うが未確認).

Binder,K. and Young,A.P. (1986)
Spin glasses: experimental facts, theoretical concepts, and open questions
Reviews of Modern Physics 58 801-976.

Bishop,Y.M.M., Finenberg,S.E. and Holland,P.W.(1975)
Discrete Multivariate Analysis: Theory and Practice
MIT Press, ¥ Cambridge, ¥ Mass.

Bouzida,D., Kumar,S and Swendsen,R.H.(1992)
Efficient Momte Carlo methods for the computor simulation of biological molecules
Physical Review A 45 8894-8901.

Bruce,A.D. and Saad,D. (1994)
Statistical mechanics of hypothesis evaluation
Journal of Physics A (Mathematical and General) 27 3355-3363.

Camevali,P, Coletti,L and Patarnello,S (1985)
Image processing by simulated annealing
IBM J. Res. Develop. 29 NO.6 569-579.

Carlin,B.P. and Chib,S. (1995)
Bayesian model choice via Markov chain Monte Carlo Methods
The Journal of the Royal Statistical Society B 57 No.3 473-484.

Carlin,B.P., Polson,N.G. and Stoffer,N.G.(1992)
A Monte Carlo approach to nonnormal and nonlinear state-space modeling
Journal of the American Statistical Association 87 493-500.

Carrington,P.J., Heil,G.H. and Berkowitz,S.D.(1979/80)
A Goodness-of-fit index for blockmodels
Social Networks 2 219-234.

Celeux,G.(1986)
Validity test in cluster analysis using a probabilistic teacher algorithm
Compstat, 86 163-168 Springer-Verlag, Berlin.

Celeux,G. and Diebolt,J.(1985)
The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for mixture problem
Computational Statistics Quarterly 2 73-82.

Celeux,G. and Govaert,G. (1992)
A classification EM algorithm for clustering and two stochastic versions
Computational Statistics and Data Analysis 14 315-332.

Celeux,G. and Govaert,G. (1993)
Comparison of the mixture and the classification maximum likelihood in cluster analysis
J. Statist. Comput. Simul. 47 127-146.

Chan,K.S. and Ledolter,J. (1995)
Monte Carlo EM estimation for time series models involving counts
Journal of the American Statistical Association 90, 242-252.

Charter,M.K. (1992)
Modelling drug behaviour in the body with MAXENT
in Maximum Entropy and Bayesian Methods, Seattle, 1991, pp.287-302,
Eds. C.R Smith et al., 1992 by Kluwer.

Chellappa,R. and Jain,A.K. (eds.) (1993)
Markov Random Fields: ¥ Theory and Application
Academic Press, San Diego.

Cheng,B. and Titterington,D.M. (1994)
Neural networks: a review from a statistician perspective
Statistical Science 9 2-54.

Clarke,B. and Barron,A.R. (1994)
Jeffreys' prior is asymptotically least favorable under entropy risk
Journal of Statistical Planning and Inference 41, 37-60.

Cross,G.R. and Jain,A.K. (1983)
Markov random field texture models
IEEE Transactions on Pattern Analysis and Machine Intelligence 5 25-39.

Day, N.E.(1969)
Estimating the components of a mixture of normal distributions
Biometrika 56 463-474.

Delarue,M. and Koehl,P. (1996)
The inverse protein folding problem : Self consistent mean field optimization of a structure specific mutation matrix,
in Pacific Symposium on Biocomputing '97, pp.109-121
Eds. R.B Altman, A.K.Dunker, L.Hunter, T.E. Klein
World Scientific

Dempster,A.P., Laird,N.M., Rubin,D.B. (1977)
Maximum likelihood from incomplete data via the EM algorithm
The Journal of the Royal Statistical Society B 39 1-38 (with discussion).

Derin,H., Elliott,H., Cristi,R. and Geman,D.(1984)
Bayes smoothing algorithms for segmentation of binary images modeled by Markov random fields
IEEE Transactions on Pattern Analysis and Machine Intelligence 6 707-720.

Derin,H. and Elliott,H.(1987)
Modeling and segmentation of noisy and textured images using Gibbs random fields
IEEE Transactions on Pattern Analysis and Machine Intelligence 9 39-55.

Deutsch,J.M. and Kurosky,T. (1996)
New Algorithm for Protein Design,
Physical Review Letters, 76, pp.323-326.

Devijver,P.A. and Dekesel,M.M. (1987)
Learning the parameters of a hidden Markov random field image model: A simple example
in NATO ASI series F30 ¥ Pattern Recognition Theory and Applications
Eds. P.A.Devijver and J.Kittler
Springer-Verlag, Berlin.

Diebolt,J and Robert,C.P.(1994)
Estimation of finite mixture distributions through Bayesian sampling
The Journal of the Royal Statistical Society B 56 363-375.

Dinten,J.M., Guyon,X., and Yao,J.F. (1991)
On the choice of the regularization parameter: The case of binary images in the Bayesian restoration framework
in Spatial Statistics and Imaging ¥ Ed. A. Possolo
LECTURE NOTES-MONOGRAPH SERIES Vol.20
Institute of Mathematical Statistics, Hayward, California.

Dubes,R.C. and Jain,A.K.(1989)
Random field models in image analysis
Journal of Applied Statistics 16 No.2 131-164.

Everitt,B.S. and Hand,D.J.(1981)
Finite mixture distributions
Chapman and Hall, ¥ London New York.

Escobar,M.D.and West,M. (1995)
Bayesian density estimation and inference using mixtures
Journal of the American Statistical Association 90 577-588.

Felsenstein,J. and Churchill,G.A. (1996)
A hidden Markov model approach to variation among sites in rate of evolution
Mol. Biol. Evol., 13, pp.93-104.

Frank,O. and Strauss,D.(1986)
Markov graphs
Journal of the American Statistical Association 85 832-842.

藤井宏 (1996)
脳の符号化機構への新しいシナリオ
科学, Vol.66, pp.880-888, (1996年12月号), 岩波.

Fushimi,M. and Tezuka,S. (1981)
Generation of the pseudo-random numbers which distribute uniformly in the high dimensional space.
Applied Statistics 10 151-163 (in Japanese).

伏見正則,手塚集(1981)
多次元分布が一様な疑似乱数列の生成法
応用統計学 10 151-163 .

Geiger,D. and Girosi,F.(1991)
Parallel and deterministic algorithms for MRF's : surface reconstruction and integration
IEEE Transactions on Pattern Analysis and Machine Intelligence 13 401-412.

Gelfand,A.E. and Smith,A.F.M.(1990)
Sampling-based approaches to calculating marginal densities
Journal of the American Statistical Association 85 398-409.

Gelfand,A.E., Hills,S.E., Racine-poon,A. and Smith,A.F.M.(1990)
Illustration of Bayesian inference in normal data models using Gibbs sampling
Journal of the American Statistical Association 85 972-985.

Geman,D. and Geman,S.(1986)
Bayesian image analysis
in Disordered Systems and Biological Organization ¥ NATO ASI series F20
Eds. E.Bienenstock et al.,¥ Springer-Verlag, ¥ Berlin.

Geman,D., Geman,S. and Graffigne,C.(1987)
Locating texture and object boundaries
in Pattern Recognition Theory and Applications ¥ NATO ASI series F30
Eds. P.A.Devijver and J.Kittler,¥ Springer-Verlag, ¥ Berlin.

Geman,D., Geman,S., Graffigne,C. and Dong,P. (1990)
Boundary detection by constrained optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence 12 609-628.

Geman,S. and Geman,D.(1984)
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
IEEE Transactions on Pattern Analysis and Machine Intelligence 6 721-741.

Geman,S. and McClure,D.E. (1987@) ???
Bayesian image analysis : An application to single photon emission tomography
Proc. American Statistical Association
Statistical Computing Section, 12-18.

Geman,S. and McClure,D.E. (1987)
Statistical methods for tomographic image reconstruction
Proc. of the 46th Session of the ISI, Bulletin of the ISI, Vol.52.
Bulletin of the International Statistical Institute, 52, 5-21.

George,E.I. and McCulloch,R.E. (1993)
Variable selection via Gibbs sampling,
Journal of the American Statistical Association 88 881-889.

Gerstein,G.L., Bedenbaugh,P. and Aertsen,M.H.J.,
Neuronal assemblies (1989)
IEEE Transaction on Biomedical Engneering, 36, pp.4-14.

Geyer,C.J.(1991)
Markov chain Monte Carlo maximum likelihood
in Computing Science and Statistics:
Proc. of the 23rd symposium on the interface(Ed. E.M.Keramides)
pp.156-163 ¥ Interface Foundation, ¥ Fairfax Station, ¥ Va.

Geyer,C.J.(1992)
Practical Markov chain Monte Carlo (with discussion)
Statistical Science 7 473-511.

Geyer,C.J. (1996)
Estimation and Optimization of functions
pp.241-255 in { ¥it Markov Chain Monte Carlo in Practice }
eds. Gilks,W.R., Richardson,S., and Spiegelhalter,D.J.
, Chapman and Hall, London.

Geyer,C.J. and Thompson,E.A.(1992)
Constrained Monte Carlo maximum likelihood for dependent data (with discussion)
Journal of Royal Statistical Society B 54 657-699.

Geyer,C.J. and Thompson,E.A. (1995)
Annealing Markov chain Monte Carlo with applications to ancestral inference
Journal of the American Statistical Association 90 909-920.

Gidas,B (1989)
A renormalization group approach to image processing problems
IEEE Transactions on Pattern Analysis and Machine Intelligence 11 164-180.

Gilks,W.R., Clayton,D.G., Spiegelhalter,D.J. Best,N.G., McNEIL,A.J., Sharples,L.D. and Kirby,A.J.(1993)
Modelling complexity: Applications of Gibbs sampling in medicine
Journal of Royal Statistical Society B 55 39-52.

Gilks,W., Spiegelhalter,D. and Richardson,S.(eds)(1996)
Markov Chain Monte Carlo in practice
Chapman and Hall, ¥ London New York.

Good,I.J.(1965)
The Estimation of Probabilities
MIT Press, ¥ Cambridge, ¥ Mass.

Goodman,J. and Sokal,A.D.(1989)
Multigrid Monte Carlo method. Conceptual foundations
Physical Review D 40 2035-2071.

Gordon,A.D. (1981)
Classification
Monographs on Applied Probability and Statistics
Chapman and Hall.

Gordon,K and Smith,A.F.M.(1990)
Modeling and monitoring biomedical time series
Journal of the American Statistical Association 85 328-337.

Goldman,N., Thorne,J.L and Jones,D.T. (1996)
Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses
Journal of Molecular Biology, 263, pp.196-208.

Gray,A.J.(1993)
In discussion on the meeting on the Gibbs sampler and other Markov chain Monte Carlo Methods
Journal of Royal Statistical Society B 55 58-61.

Gray,A.J., Kay,J.W and Titterington,D.M.(1992)
On the estimation of noisy binary Markov random fields
Pattern recognition 25 749-768.

Gray,A.J., Kay,J.W and Titterington,D.M.(1994)
An emphirical study of the simulation of various models used for images
IEEE Transactions on Pattern Analysis and Machine Intelligence 16 pp.507-513.

Greig,D.M. ,Porteous,B.T. and Seheult,A.H.(1989)
Exact maximum a posteriori estimation for binary images
Journal of Royal Statistical Society B, 51 271-279.

Grenandar,U. and Keenan,D.M. (1989)
Towards automated image understanding
Journal of Applied Statistics 16 No.2 207-222.

Grenandar,U. and Miller,I.M. (1994)
Representations of knowledge in complex systems
Journal of Royal Statistical Society B, 56 549-603 (with discussion).

Gull,S.F. (1989)
Developments in maximum entropy data analysis
in Maximum Entropy and Bayesian Methods, pp.53-71
ed. Skilling,J., Kluwer Academic.

Hansell,S.(1984)
Cooperative groups, weak ties and integration of peer friendships
Social Psychology Quarterly 47, 316-328
(未入手、Wang and Wong(1987)に引用).

韓太舜(はん・てすん) (1987)
数理科学 No.290, 1987年 8月号 (特集・情報圧縮),pp.5-15.

長谷川政美, 岸野洋久 (1996)
分子系統学, 岩波書店.

長谷川政美,種村正美(1986)
なわばりの生態学 生態のモデルと空間パターンの統計
東海大学出版会.

Hastings,W.K. (1970)
Monte Carlo sampling methods using Markov chains and their applications
Biometrica 57 97-109.

Heil,G.H. and White,H.C. (1976)
An algorithm for finding simultaneous homomorphic correspondences between graphs and their image graph
Behavioral science 21 26-35.

Higdon,D.(1993)
In discussion on the meeting on the Gibbs sampler and other Markov chain Monte Carlo Methods
Journal of Royal Statistical Society B 55 78-78.
@

Higuchi,T., Kita,K. and Ogawa,T.(1988)¥ Applied Optics 27 pp.4514-4519.

Hinton,G.E. and Sejnowski,T.J.(1986)
Learning and relearning in Boltzmann machines
in Parallel Distributed Processing Vol.1
Eds. E. Rumelhart and J.L. McClelland.
MIT Press, ¥ Cambridge, ¥ Mass.

Hirosawa,M., Feldmann,R.J., Rawn,D., Ishikawa,M. Hoshida, M., Micheals,G. (1992)
Folding simulation using temperature parallel simulated annealing
Proceedings of the international conference on fifth generation computer systems 1992, ICOT, pp.300-306.

Holland,P.W.,Laskey,K.B., and Leinhardt,S.(1983)
Stochastic block models: first steps
Social Networks 5 109-137.

本郷節之,川人光男,乾俊郎,三宅誠(1991)
エネルギー学習機能をもつ局所並列確率アルゴリズムによる輪郭線抽出
電子情報通信学会論文誌 D-‾II vol.J74 NO.3 348-356.

Hopfield,J.J. and Tank,D.W.(1985)
``Neural'' computation of decisions in optimization problems,
Biological Cybernetics 52 141-152.

Hopfield,J.J. and Tank,D.W.(1986)
Collective computation with continous variables
in Disordered Systems and Biological Organization ¥ NATO ASI series F20
Eds. E.Bienenstock et al.
Springer-Verlag Berlin.

@@ 星田 ほか (1994)
遺伝子情報処理への挑戦, 共立出版

Hrycej,T. (1990)
Gibbs sampling in Bayesian network
Artificial Intelligence 46 351-363.

福島孝治(1994)
物理学会 秋の分科会.

Hukushima,K and Nemoto,K. (1996)
Exchange Monte Carlo method and application to spin glass simulations
Journal of Physical Society of Japan, 65, 1604-1608.

Iba,Y.(1989)
Bayesian statistics and statistical mechanics
in cooperative dynamics in complex physical systems
Ed. H.Takayama, ¥ Springer-Verlag, ¥ Berlin.

伊庭幸人(1990),
統計物理と統計的情報処理(1.00版),
--- 大規模・非ガウスモデルをめぐる話題 ---,
(図を除いて¥verb+http://www.ism.ac.jp/‾iba/+で入手可能).

伊庭幸人(1991a)
メトロポリス的モンテカルロ法の巨視的パラメータ推定への応用
-- 2次元イジング模型の場合 --
統計数理 39 No.1 1-21.

伊庭幸人(1991b)
メトロポリス的モンテカルロ法の擬ベイズ法への応用
-- 変化点問題を例として --
統計数理 39 No.2 225-244.

Iba,Y.(1992)
An application of Metropolis-type algorithm to a complex classification problem
ISM Research Memorandum No.440.

伊庭幸人(1993a)
ベイズ統計と統計物理 ¥ -有限温度での情報処理-
物性研究 60-6 (1993年9月号) 677-699

伊庭幸人(1993b)
統計数理研究所 年度末発表会(1992年度)要旨
メトロポリス的モンテカルロ法の緩和について
統計数理 41 No.1 65-67.

伊庭幸人(1994)
統計数理研究所 年度末発表会(1993年度)要旨
マルコフ連鎖モンテカルロ法と適応的デザイン
統計数理 42 No.2 343-345.

伊庭幸人(1996)
学習と階層 --- ベイズ統計の立場から ---
ISM Research Memorandum No.558
「物性研究」65-5(1996年2月号) 657-677.

伊庭幸人(1996)
ベイズ統計と統計物理(物性研究1993年9月号)への訂正と追加
「物性研究」65-5(1996年2月号)678-685.

伊庭幸人(1996)
基礎的問題から見た情報統合
人工知能学会誌 Vol.11 No.2 (1996年3月号) 193-200.

伊庭幸人(1996)
マルコフ連鎖モンテカルロ法とその統計学への応用
統計数理 44 No.1 49-84.

伊庭幸人 (1996)
統計学者・数理工学者のための統計物理入門 --- 暫定版 (2.1版),
--- 格子スピン模型とマルコフ連鎖モンテカルロ法を中心にして ---,
ISM Reseach Memorandum No.592, January, 1996.
(¥verb+http://www.ism.ac.jp/‾iba/+で図を除いて入手可能)

伊庭幸人 (1996)
回帰分析の変数選択におけるベイズ的方法, -- 平均場近似に基づくアプローチ --,
ISM Reseach Memorandum No.624, October, 1996.
SITA 96(Dec. 1996, 箱根)予稿.
(¥verb+http://www.ism.ac.jp/‾iba/+で入手可能.)

伊庭幸人 (1997)
統計科学 文献案内
ISM Reseach Memorandum No.630, January, 1997
(¥verb+http://www.ism.ac.jp/‾iba/+で入手可能.)

石黒真木夫 (1985)
ベイズ型重回帰モデル,
統計数理 33 No.1, ¥ 特集 -- エントロピー最大化原理の展開と統計モデル -- , pp.8-11.
@ 35B ??

Ishiguro,M. and Sakamoto,Y.(1983)
A Bayesian approach to binary response curve estimation
Annals of the Institute of Statistical Mathematics 35B 115-137.

石川真澄 (1991)
ネットワーク学習アルゴリズムの最近の話題
計測と制御, Vol.30, 285-290.

Ishikawa,M. (1996)
Structural learning with forgetting,
Neural Networks, Vol.9, 509-521.

石川幹人、金久實 (1993)
文字列を比較し並べる
小特集「生命の設計図に迫る!」,¥ 日本物理学会誌 Vol.48 No.5 341-343.

Kandel,D., Domany,E. and Brandt,A. (1989)
Simulation without critical slowing down: Ising and three-state Potts models
Physical Review B 40 330-344.

@ Kaneko,K. and Tsuda,I.(1994)¥ Physica D 75 pp.1-10.

金子邦彦, 津田一郎 (1996)
複雑系のカオス的シナリオ
複雑系双書 1, 朝倉書店.

Kashiwagi N. (1991)
Bayesian detection of structural changes
Annals of the Institute of Statistical Mathematics 43 77-93.

Kay.J.W. and Titterington D.M. (1986)
Image labelling and statistical analysis of incomplete data
Proc. 2nd. Int. Conf. Image processing and Applications
Conf. Publ. No.265 ¥ London Inst. Elec. Engrs. 44-48.

川人光男 (1996),
脳の計算理論,
産業図書.

@ Kawato,M. and Inui,T.(1990)¥ 電子情報通信学会論文誌 J73-D-II, pp.1111-1121.

川人光男,行場次朗,藤田一郎,乾敏郎,力丸裕 (1994)
視覚と聴覚, 岩波講座 認知科学 3, 岩波.
(特に、2章(大脳視覚野の生理学), 4章(音響・聴覚系の生理学)).

Kikuchi,M. and Okabe,Y.(1992)
Order-parameter distribution function and order of the phase transition of the ferromagnetic Potts model
Journal of Physical Society of Japan 61 3503-3510.

Kikuchi,M, Ito,N. and Okabe,Y. (1994)
Statistical dependence and related topics
to be puplished in Computer Simulation in Condensed Matter Physics VII‾
Eds. D.P.Landau, K.K.Mon, and H.B.Sch¥"{u}ttler, Springer-Verlag, Berlin.

Kimura and Taki (1990)
Time-homogeneous parallel annealing algorithm
Proc. Comp. Appl. Math. (IMACS'91), 13, pp.827-828.
( !! I have never seen this vesion of the paper !!).

木村宏一, 瀧和男(1990)
時間的一様な並列アニーリングアルゴリズム
電子情報通信学会 NC-90-1 1-8.

Kimura,K. and Taki,K (1990)
On a time-homogeneous parallel annealing algorithm
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iba@ism.ac.jp, 1996