Biomedical Engineering Reference
In-Depth Information
99. Ron D, Singer Y, Tishby N. 1996. The power of amnesia: learning probabilistic automata with
variable memory length.
Machine Learning
25
:117-149.
100. Bühlmann P, Wyner AJ. 1999. Variable length Markov chains.
Ann Stat
27
:480-513
(http://www.stat.berkeley.edu/tech-reports/479.abstract1).
101. Kennel MB, Mees AI. 2002. Context-tree modeling of observed symbolic dynamics.
Phys Rev
E
66
:056209.
102. Crutchfield JP, Young K. 1989. Inferring statistical complexity.
Phys Rev Lett
63
:105-108.
103. Jaeger H. 2000. Observable operator models for discrete stochastic time series.
Neural Comput
12
:1371-1398 (http://www.faculty.iu-bremen.de/hjaeger/pubs/oom/neco00.pdf).
104. Littman ML, Sutton RS, Singh S. 2002. Predictive representations of state. In
Advances in
neural information processing
, pp. 1555-1561. Ed. TG Dietterich, S Becker, Z Ghahramani,
Systems 14
. MIT Press, Cambridge (http://www.eecs.umich.edu/~baveja/Papers/psr.pdf).
105. Shalizi CR, Shalizi KL. 2004. Blind construction of optimal nonlinear recursive predictors for
discrete sequences. In
Uncertainty in artificial intelligence: proceedings of the twentieth con-
ference
, pp. 504-511. Ed. M Chickering, J Halpern. AUAI Press, Arlington, VA (http://arxiv.
org/abs/cs.LG/0406011).
106. Salmon WC. 1971.
Statistical explanation and statistical relevance
. With contributions by RC
Jeffrey and JG Greeno. U Pittsburgh P.
107. Salmon WC. 1984.
Scientific explanation and the causal structure of the world
. Princeton UP,
Princeton.
108. Singh S, Littman, ML, Jong NK, Pardoe D, Stone P. 2003. Learning predictive state
representations. In
Proceedings of the twentieth international conference on machine learning
(ICML-2003)
, pp. 712-719. Ed. T Fawcett, N Mishra. AAAI Press, New York (http://www.
eecs.umich.edu/~baveja/Papers/ICMLfinal.ps.gz).
109. Upper DR. 1997.
Theory and algorithms for hidden markov models and generalized hidden
markov models
. PhD thesis, University of California, Berkeley (http://www.santafe.edu/
projects/CompMech/ or papers/TAHMMGHMM.html).
110. Dupont P, Denis F, Esposito Y. 2004. Links between probabilistic automata and hidden
Markov models: probability distributions, learning models and induction algorithms.
Pattern
Recognit
Forthcoming (http://www.info.ucl.ac.be/people/pdupont/pdupont/postscript/Links_
PA_HMM_preprint.ps.gz)
111. Jaeger H. 1999.
Characterizing distributions of stochastic processes by linear operators
.
Technical Report 62, German National Center for Information Technology (http://www.
faculty.iu-bremen.de/hjaeger/pubs/oom_distributionsTechRep.pdf).
112. Jaeger H. 2000.
Modeling and learning continuous-valued stochastic processes with OOMs
.
Technical Report 102, German National Center for Information Technology (http://
www.faculty.iu-bremen.de/hjaeger/pubs/jaeger.00.tr.contoom.pdf).
113. Crutchfield JP. 1992. Unreconstructible at any radius.
Phys Lett A
171
:52-60.
114. Bollt EM, Stanford T, Lai Y-C, Zyczkowski K. 2000. Validity of threshold-crossing analysis
of symbolic dynamics from chaotic time series.
Phys Rev Lett
85
:3524-3527.
115. Bollt EM, Stanford T, Lai Y-C, Zyczkowski K. 2001. What symbolic dynamics do we get with
a misplaced partition? On the validity of threshold crossing analysis of chaotic time-series.
Physica D
154
:259-286.
116. Kitchens BP. 1998.
Symbolic dynamics: one-sided, two-sided and countable state markov
shifts
. Springer, Berlin.
117. Kennel MB, Buhl M. 2003. Estimating good discrete partitions from observed data: symbolic
false nearest neighbors.
Phys Rev Lett
91
:084102 (http://arxiv.org/abs/nlin.CD/0304054).
118. Hirata Y, Judd K, Kilminster D. 2004. Estimating a generating partition from observed time
series: Symbolic shadowing.
Phys Rev E
70
:016215.
119. Crutchfield JP, Packard NH. 1983. Symbolic dynamics of noisy chaos.
Physica D
7
:201-223.
120. Moore C. 1997. Majority-vote cellular automata, Ising dynamics, and P-completeness.
J Stat
Phys
88
:795-805 (http://arxiv.org/abs/cond-mat/9701118).