Biomedical Engineering Reference
In-Depth Information
173. Abbott LF, Sejnowski TJ, eds. 1998.
Neural codes and distributed representations: founda-
tions of neural computation
. MIT Press, Cambridge.
174. Billingsley P. 1965.
Ergodic theory and information
. Wiley, New York.
175. Katok AB, Hasselblatt B. 1995.
Introduction to the modern theory of dynamical systems
. Cam-
bridge UP, Cambridge.
176. Solomonoff RJ. 1964. A formal theory of inductive inference.
Inf Control
7
:1-22, 224-254
(http://world.std.com/~rjs/pubs.html).
177. Li M, Vitanyi PMB. 1993.
An introduction to Kolmogorov complexity and its applications
.
Springer, New York.
178. Zurek WH. 1998. Algorithmic randomness, physical entropy, measurements, and the demon of
choice. E-print, arxiv.org (http://arxiv.org/abs/quant-ph/9807007).
179. Bennett CH. 1985. Dissipation, information, computational complexity and the definition of
organization. In
Emerging syntheses in science
, pp. 215-234. Ed D Pines. Santa Fe Institute,
Santa Fe, NM.
180. Bennett CH. 1986. On the nature and origin of complexity in discrete, homogeneous locally-
interacting systems.
Found Phys
16
:585-592.
181. Bennett CH. 1990. How to define complexity in physics, and why. In
Complexity, entropy, and
the physics of information
, pp. 137-148. Ed. WH Zurek. Addison-Wesley, Reading, MA.
182. Lloyd S, Pagels H. 1988. Complexity as thermodynamic depth.
Ann Phys
188
:186-213.
183. Gács P, Tromp JT, and Vitanyi PMB. 2001. Algorithmic statistics.
IEEE Trans Inf Theory
47
:2443-2463 (http://arxiv.org/abs/math.PR/0006233).
184. Rissanen J. 1978. Modeling by shortest data description.
Automatica
14
:465-471.
185. Rissanen J. 1989.
Stochastic complexity in statistical inquiry
. World Scientific, Singapore.
186. Hraber PT, Korber BT, Wolinsky S, Erlich H, Trachtenberg E. 2003.
HLA and HIV infection
progression: application of the minimum description length principle to statistical genetics
.
Technical Report 03-04-23, Santa Fe Institute (http://www.santafe.edu/research/publications/
wpabstract/200304023).
187. Grassberger P. 1986. Toward a quantitative theory of self-generated complexity.
Int J Theor
Phys
25
:907-938.
188. Shalizi CR. 2001.
Causal architecture, complexity and self-organization in time series and
cellular automata
. PhD thesis, University of Wisconsin-Madison (http://bactra.org/thesis/).
189. Shaw R. 1984.
The dripping faucet as a model chaotic system
. Aerial Press, Santa Cruz, CA.
190. Lindgren K, Nordahl MG. 1988. Complexity measures and cellular automata.
Complex Syst
2
:409-440.
191. Li W. 1991. On the relationship between complexity and entropy for Markov chains and regu-
lar languages.
Complex Syst
5
:381-399.
192. Arnold D. 1996. Information-theoretic analysis of phase transitions.
Complex Syst
10
:143-155.
193. Bialek W, Nemenman I, Tishby N. 2001. Predictability, complexity and learning.
Neural
Comput
13
:2409-2463 (http://arxiv.org/abs/physics/0007070).
194. Crutchfield JP, Young K. 1990. Computation at the onset of chaos. In
Complexity, entropy,
and the physics of information
, pp. 223-269. Ed. WH Zurek. Addison-Wesley, Reading, MA.
195. Shalizi CR, Crutchfield JP. 2001. Computational mechanics: Pattern and prediction, structure
and simplicity.
J Stat Phys
104
:817-879 (http://arxiv.org/abs/cond-mat/9907176).
196. Shalizi CR. 2003. Optimal nonlinear prediction of random fields on networks.
Discr Math
Theor Comput Sci
AB(DMCS):11-30 (http://arxiv.org/abs/math.PR/0305160).
197. Shalizi, CR, Shalizi KL, Haslinger R. 2004. Quantifying self-organization with optimal predic-
tors.
Phys Rev Lett
93:118701 (http://arxiv.org/abs/nlin.AO/0409024).
198. Lewis HR, Papadimitriou CH. 1998.
Elements of the theory of computation
. Prentice-Hall,
Upper Saddle River, NJ.
199. Clarke RW, Freeman MP, Watkins NW. 2003. Application of computational mechanics to the
analysis of natural data: an example in geomagnetism.
Phys Rev E
67:0126203
(http://arxiv.org/abs/cond-mat/0110228).
