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Wallace C.S. Ч Statistical and Inductive Inference by Minimum Message Length
Wallace C.S. Ч Statistical and Inductive Inference by Minimum Message Length

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Ќазвание: Statistical and Inductive Inference by Minimum Message Length

јвтор: Wallace C.S.


Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning, Estimation and Model-selection, Econometrics, and Data Mining.

язык: en

–убрика: ћатематика/

—татус предметного указател€: √отов указатель с номерами страниц

ed2k: ed2k stats

√од издани€: 2005

 оличество страниц: 429

ƒобавлена в каталог: 10.12.2005

ќперации: ѕоложить на полку | —копировать ссылку дл€ форума | —копировать ID
ѕредметный указатель
AC      see УAlgorithmic complexityФ
Afterlife theory      397
AIC (Akaike information criterion)      303
Akaike information criterion (AIC)      35 303
Akaike, H.      35
Algorithmic complexity (AC)      100Ч110 311
Algorithmic complexity (AC), explanations and      118Ч121
Algorithmic complexity (AC), Shannon information versus      107Ч110
Algorithmic Probability (ALP)      406
Allison, L.      209 274
ALP (Algorithmic Probability)      406
Alphabet      58
Alternative priors      350 353
Ancillary statistic      242
Approximate theories      14
Arcs      305Ч306
Arithmetic coding      73Ч76
Arrow of Time, Thermodynamic      337Ч384
assertion      152 see first
Assertion code for probabilistic finite-state machines      308Ч309
Assertion, imprecise, of discrete parameters      284Ч286
Assertion, length of      235
Asymmetry, reasons for      367Ч369
Atom count difference      383
Bakus Ч Naur form (BNF)      306Ч307
Barron, A.R.      311
Baxter. R.A.      274
Bayes Information Criterion (BIC)      303
Bayes posterior density      182
Bayes' theorem      22
Bayesian decision theory      40Ч45
Bayesian inference      1 35Ч40
Bayesian inference, relation to      116Ч118
Bernardo, J.M.      47
Bernoulli sequence      147
Best explanation of data      1
Beta density      246
beta function      246
Beta prior density      47
Bhansali, R.J.      35
Bibby, J.M.      300
BIC (Bayes Information Criterion)      303
Binary codes      59Ч63
binary digits      59
Binary sequence segmentation problem      321Ч326
Binary tree codes      95Ч96
Binomial distributions      24
Binomial distributions, irregularities in      248
Binomial distributions, negative      253
Binomial example      157Ч160
Binomial example, using sufficient statistics      163Ч164
Bit      66
Blurred images      208Ч209
BNF (Bakus Ч Naur form)      306Ч307
Boltzmaiiii, Stefan      338Ч339
Boltzmann's constant      88
Boulton, D.M.      81 401
Boundary rule, for growing data groups      171Ч173
Boundary rule, for ideal data groups      198Ч199
Carnap, R.      5
Categories      315
Categories, regression in      320
Cauchy density      32
Causal explanations      365Ч367
Causal nets      326Ч336
Cause, effect and      365Ч367
Chaitin, G.J.      3Ч4 57 102 109 275 401
Chickering, D.M.      330
ChiSquared form      34
Class distributions at leaves, coding      317 318
Class labels      276
Class labels, Fisher Information with      291 293
Class labels, surrogate estimate      288Ч289
Class proportions      294
Classification trees and nets      314 320
Classified models, summary of      293 294
Classified models, unclassified models versus      295 297
Cleary. J.G.      73
Clocks, example of      353Ч355
Closed systems      339 340
Coarse data, normal distribution with      265Ч266
Code length of imprecise discrete estimates      286Ч288
Code tree      61
Code word in Huffman code      282
Codeable estimates      213Ч215
codes      59
Codes, binary tree      95Ч96
Codes, feasible, for infinite sets      96 98
Codes, for infinite sets      91Ч92
Codes, non-biuar.y      77Ч78
Codes, optimal      see УOptimal codesФ
Codes, universal      98 100
Codes, universal, in theory descriptions      115Ч116
Coding probability      149Ч150
Coding probability, prior probability density and      222
Coding scheme. MML      222Ч228
Coding transitions      312Ч313
Coding trick      281Ч284 293
Coding, arithmetic      73 76
Coding, class distributions at leaves      317Ч318
Coding, multi-word messages      72 73
Coding, of data      146Ч147
Coding, of inferences      148Ч150
Coding, random, of estimates      210
Coding, tree structure      316Ч317
Collision table      370 375
Communication of information      57Ч58
Complexity Approximation Principle      405
Computer process, past of a      375
Concatenation      119
Concentration parameter      259
Conditional probability      21
Confidence      30
Confidence interval      30
Conjugate priors      46Ч48
Conjugate priors, for multivariate Normal distribution      261Ч264
Conjugate priors, for Normal distribution      258Ч264
Conjugate priors, invariant      52Ч53 261
Consequential propositions      7
Continuous data, SMML explanation for      166Ч187
Continuous distributions      24Ч25
Continuous random variables      20
Conway, J.H.      178 257
Cost function      54
Counter-instances      10
Cover, T.M.      98 311
Cross-Validation (CV) criterion      321 323
Curved-prior message length, MML      236Ч237
Cut points      321Ч322
CV (Cross-Validation) criterion      321Ч322
DAG (directed acyclic graph)      326Ч327
Data acquisition      397
Data groups      222
Data groups, growing, boundary rule for      171Ч173
Data groups, ideal      198Ч199
Data representation invariance      187Ч188
Data, coarse, normal distribution with      265Ч266
Data, coding of      146Ч147
Data, continuous, SMML explanation for      166Ч187
Data, discrete, SMML explanation for      153Ч166
Data, explanation of      359
Data, perturbed, normal distribution with      264Ч265
Data, possible, set X of      144Ч145
Data, probabilistic model of      146
decimal numbers      59
Decision graphs      318Ч320
Decision tree explanation      315Ч316
Decision trees      315
Deduction, of past disorder      345Ч355
Deduction, uses of      361Ч302
Deduction, with deterministic laws      346Ч348
Deduction, with non-deterministic laws      348Ч350
Deductive reasoning      5
Defining propositions      7
density      25
Descriptive MML theory      385Ч399
detail      152 see second
Detail length      235 325Ч326
Deterministic laws      343Ч344
Deterministic laws, deduction with      346Ч348
Deterministic laws, induction of the past with      363Ч365
Devolution      346
diatomic molecules      374Ч375
Dirac delta function      193
Directed Acyclic Graph (DAG)      326Ч327
Disc collision model      351
Discrete data, SMML explanation for      153Ч166
Discrete distributions      23Ч24
Discrete estimates, imprecise, code length of      286Ч288
Discrete hypothesis sets      156
Discrete parameters, imprecise assertion of      284Ч286
Discrete random variables      20
Disorder      337
Disorder, entropy as measure of      341Ч343
Disorder, past, deduction of      345Ч355
Dissipation      361
Dissipative laws      345
Distributions      23
Distributions, binomial      see УBinomial distributionsФ
Distributions, distributions information content of      81Ч87
Distributions, entropy of      89
Distributions, infinite entropy      94
Distributions, multinomial      see УMultinomialФ
Distributions, Normal      see УNormal distributionsФ
Distributions, predictive      206
Distributions, probability      see УProbability distributionsФ
Distributions, uniform, of known range, mean of      183Ч187
Dowc, D.L.      203 209 216 219 252 268Ч269 274 323Ч326
Dowe's approximation to message length      209Ч213
Dowe's construction, uncertainty regions via      216
Downham, D.Y.      35
Educated Turing machines (ETM)      130Ч131
Effect, cause and      365Ч367
EM (Expectation Maximization) algorithm      276 279
Empirical Fisher Information      240Ч245
Empirical Fisher Information, transformation of      243Ч244
Empirical information matrix      241
entropy      51 87Ч91 337
Entropy, as measure of disorder      341Ч343
Entropy, increasing      343Ч344 376 384
Entropy, of distributions      89
Entropy, time reversal and      350
Equilibrium      341Ч342
Equivalence sets      329
Equivalence, partial order      330
Equivalence, structural      330 331
escape sequence      136
Estimate spacing, precision of      238Ч240
Estimate(s)      30
Estimate(s), class label, surrogate      288Ч289
Estimate(s), codeable      213Ч215
Estimate(s), imprecise discrete, code length of      286Ч288
Estimate(s), random coding of      210
Estimate(s), Schou      268
Estimation of Normal mean with Normal prior      173Ч177
Estimator      31
Estimator function      154
ETM (Educated Turing Machines)      130Ч131
Euclidean distance      178
Euler's constant      179
Evolution probability      348
Evolutionary induction      396Ч397
Exceptions      18
Expectation      25Ч27
Expectation Maximization (EM) algorithm      276Ч279
Expected string length      64
Expected value of loss function      189
experiments      397Ч399
Explanation length      143 160 331
Explanation message      16Ч19
Explanation message, shortest      143
Explanation structure, three-part      242
Explanation(s)      14Ч19
Explanation(s), algorithmic complexity and      118Ч121
Explanation(s), first part of      112Ч114 121Ч123
Explanation(s), of data      359
Explanation(s), second part of      110Ч112 120Ч121
Explanatory power      13 16
Factor analysis model      297Ч303
Factor analysis model, defining equations for      299
Factor analysis model, MML      300Ч303
Factor loads      298
Factor scores      298
Fair's algorithm      164Ч165
Fair, G.E.      159 164Ч165 172
Falsifiable propositions      10Ч11
Falsifying data      12
Familv of models      28
Fano, R.M.      70Ч72
Feasible codes for infinite sets      96 98
Fermi Ч Dirac statistics      378
Finite-State Machines (FSMs)      127Ч128 305Ч314
Finite-state machines (FSMs), alternative expression for      228Ч229
Finite-state machines (FSMs), empirical      see УEmpirical Fisher InformationФ
Finite-state machines (FSMs), Fisher determinant      227
Finite-state machines (FSMs), Fisher Information      48 225 411Ч412
Finite-state machines (FSMs), for mixtures      290Ч291
Finite-state machines (FSMs), less-rednndant code for      309Ч310
Finite-state machines (FSMs), safer empirical approximation to      244Ч245
Finite-state machines (FSMs), transparency and redundancy in      310 312
Finite-state machines (FSMs), with class labels      291Ч293
Fisher matrix      232
Fitzgibbon, h.      209 274
Fnput tape, Turing machines      101
Formula I1A      226
Formula I1A, for many parameters      210Ч241
Formula I1B      226
Formula I1C      243
Freeman, P.R.      226Ч227 300
FSMs      see УFinite-state machinesФ
Function approximation      272Ч275
future      368
Gaines, B.R.      313
Gammerman, A.      405
Gas simulations      370Ч375
Geometric constants      257Ч258
Glymour, C      327
God theory      397
Grammars, regular      see УRegular grammarsФ
Griinwald, P.D.      227
Group      50
Growing data groups, boundary rule for      171Ч173
Hexagonal Voronoi regions      181
Huffman code      70Ч71
Huffman code, code word in      282
Huffman. D.A.      70Ч73 78 103 107 282 284Ч285
Human induction      394 396
Hutter, M.      403Ч405
Hypothesis space, partitions of      213Ч215
Hypothesis space, uncertainty regions in      214Ч215
Ideal data groups      198Ч199
Ideal group (IG) estimator      197Ч200
Ideal group (IG) estimator for Neyman Ч Scott problem      201Ч202
IG      see УIdeal group estimatorФ
Images, blurred      208Ч209
Imprecise assertion of discrete parameters      284Ч286
Imprecise discrete estimates, code length of      286Ч288
Independence      22
Induction      1
Induction, evolutionary      396 397
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