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Editorial Reviews
Review
"...tantalizing ideas...one of the most useful and least familiar applications of Bayesian theory...Probability Theory [is] considerably more entertaining reading than the average statistics textbook...the conceptual points that underlie his attacks are often right on."
Science
"This is a work written by a scientist for scientists. As such it is to be welcomed. The reader will certainly find things with which he disagrees, but he will also find much that will cause him to think deeply not only on his usual practice by also on statistics and probability in general. Probability Theory: the Logic of Science is, for both statisticians and scientists, more than just 'recommended reading': it should be prescribed."
Mathematical Reviews
"...the rewards of reading Probability Theory can be immense."
Physics Today, Ralph Baierlein
“This is not an ordinary text. It is an unabashed, hard sell of the Bayesian approach to statistics. It is wonderfully down to earth, with hundreds of telling examples. Everyone who is interested in the problems or applications of statistics should have a serious look.”
SIAM News
"[T]he author thinks for himself...and writes in a lively way about all sorts of things. It is worth dipping into it if only for vivid expressions of opinion...There are many books on Bayesian statistics, but few with this much color."
Notices of the AMS
Product Description
Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.
Product Details
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Probability Theory The Logic of Science~E.T.Jaynes.2003.pdf
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3.6 Probability as a mathematical tool 68
3.7 The binomial distribution 69
3.8 Sampling with replacement 72
3.8.1 Digression: a sermon on reality vs. models 73
3.9 Correction for correlations 75
3.10 Simplification 81
3.11 Comments 82
3.11.1 A look ahead 84
4 Elementary hypothesis testing 86
4.1 Prior probabilities 87
4.2 Testing binary hypotheses with binary data 90
4.3 Nonextensibility beyond the binary case 97
4.4 Multiple hypothesis testing 98
4.4.1 Digression on another derivation 101
4.5 Continuous probability distribution functions 107
4.6 Testing an infinite number of hypotheses 109
4.6.1 Historical digression 112
4.7 Simple and compound (or composite) hypotheses 115
4.8 Comments 116
4.8.1 Etymology 116
4.8.2 What have we accomplished? 117
5 Queer uses for probability theory 119
5.1 Extrasensory perception 119
5.2 Mrs Stewart’s telepathic powers 120
5.2.1 Digression on the normal approximation 122
5.2.2 Back to Mrs Stewart 122
5.3 Converging and diverging views 126
5.4 Visual perception – evolution into Bayesianity? 132
5.5 The discovery of Neptune 133
5.5.1 Digression on alternative hypotheses 135
5.5.2 Back to Newton 137
5.6 Horse racing and weather forecasting 140
5.6.1 Discussion 142
5.7 Paradoxes of intuition 143
5.8 Bayesian jurisprudence 144
5.9 Comments 146
5.9.1 What is queer? 148
6 Elementary parameter estimation 149
6.1 Inversion of the urn distributions 149
6.2 Both N and R unknown 150
6.3 Uniformprior 152
6.4 Predictive distributions 1546.5 Truncated uniformpriors 157
6.6 A concave prior 158
6.7 The binomial monkey prior 160
6.8 Metamorphosis into continuous parameter estimation 163
6.9 Estimation with a binomial sampling distribution 163
6.9.1 Digression on optional stopping 166
6.10 Compound estimation problems 167
6.11 A simple Bayesian estimate: quantitative prior information 168
6.11.1 Fromposterior distribution function to estimate 172
6.12 Effects of qualitative prior information 177
6.13 Choice of a prior 178
6.14 On with the calculation! 179
6.15 The Jeffreys prior 181
6.16 The point of it all 183
6.17 Interval estimation 186
6.18 Calculation of variance 186
6.19 Generalization and asymptotic forms 188
6.20 Rectangular sampling distribution 190
6.21 Small samples 192
6.22 Mathematical trickery 193
6.23 Comments 195
7 The central, Gaussian or normal distribution 198
7.1 The gravitating phenomenon 199
7.2 The Herschel–Maxwell derivation 200
7.3 The Gauss derivation 202
7.4 Historical importance of Gauss’s result 203
7.5 The Landon derivation 205
7.6 Why the ubiquitous use of Gaussian distributions? 207
7.7 Why the ubiquitous success? 210
7.8 What estimator should we use? 211
7.9 Error cancellation 213
7.10 The near irrelevance of sampling frequency distributions 215
7.11 The remarkable efficiency of information transfer 216
7.12 Other sampling distributions 218
7.13 Nuisance parameters as safety devices 219
7.14 More general properties 220
7.15 Convolution of Gaussians 221
7.16 The central limit theorem 222
7.17 Accuracy of computations 224
7.18 Galton’s discovery 227
7.19 Population dynamics and Darwinian evolution 229
7.20 Evolution of humming-birds and flowers 2319.12 Comparison of psi and chi-squared 300
9.13 The chi-squared test 302
9.14 Generalization 304
9.15 Halley’s mortality table 305
9.16 Comments 310
9.16.1 The irrationalists 310
9.16.2 Superstitions 312
10 Physics of ‘randomexperiments’ 314
10.1 An interesting correlation 314
10.2 Historical background 315
10.3 How to cheat at coin and die tossing 317
10.3.1 Experimental evidence 320
10.4 Bridge hands 321
10.5 General randomexperiments 324
10.6 Induction revisited 326
10.7 But what about quantumtheory? 327
10.8 Mechanics under the clouds 329
10.9 More on coins and symmetry 331
10.10 Independence of tosses 335
10.11 The arrogance of the uninformed 338
Part II Advanced applications
11 Discrete prior probabilities: the entropy principle 343
11.1 A new kind of prior information 343
11.2 Minimum _p2i 345
11.3 Entropy: Shannon’s theorem 346
11.4 The Wallis derivation 351
11.5 An example 354
11.6 Generalization: a more rigorous proof 355
11.7 Formal properties of maximum entropydistributions 358
11.8 Conceptual problems – frequency correspondence 365
11.9 Comments 370
12 Ignorance priors and transformation groups 372
12.1 What are we trying to do? 372
12.2 Ignorance priors 374
12.3 Continuous distributions 374
12.4 Transformation groups 378
12.4.1 Location and scale parameters 378
12.4.2 A Poisson rate 382
12.4.3 Unknown probability for success 382
12.4.4 Bertrand’s problem 386
12.5 Comments 39413 Decision theory, historical background 397
13.1 Inference vs. decision 397
13.2 Daniel Bernoulli’s suggestion 398
13.3 The rationale of insurance 400
13.4 Entropy and utility 402
13.5 The honest weatherman 402
13.6 Reactions to Daniel Bernoulli and Laplace 404
13.7 Wald’s decision theory 406
13.8 Parameter estimation for minimumloss 410
13.9 Reformulation of the problem 412
13.10 Effect of varying loss functions 415
13.11 General decision theory 417
13.12 Comments 418
13.12.1 ‘Objectivity’ of decision theory 418
13.12.2 Loss functions in human society 421
13.12.3 A new look at the Jeffreys prior 423
13.12.4 Decision theory is not fundamental 423
13.12.5 Another dimension? 424
14 Simple applications of decision theory 426
14.1 Definitions and preliminaries 426
14.2 Sufficiency and information 428
14.3 Loss functions and criteria of optimum performance 430
14.4 A discrete example 432
14.5 How would our robot do it? 437
14.6 Historical remarks 438
14.6.1 The classical matched filter 439
14.7 The widget problem 440
14.7.1 Solution for Stage 2 443
14.7.2 Solution for Stage 3 445
14.7.3 Solution for Stage 4 449
14.8 Comments 450
15 Paradoxes of probability theory 451
15.1 How do paradoxes survive and grow? 451
15.2 Summing a series the easy way 452
15.3 Nonconglomerability 453
15.4 The tumbling tetrahedra 456
15.5 Solution for a finite number of tosses 459
15.6 Finite vs. countable additivity 464
15.7 The Borel–Kolmogorov paradox 467
15.8 The marginalization paradox 470
15.8.1 On to greater disasters 47415.9 Discussion 478
15.9.1 The DSZ Example #5 480
15.9.2 Summary 483
15.10 A useful result after all? 484
15.11 How to mass-produce paradoxes 485
15.12 Comments 486
16 Orthodox methods: historical background 490
16.1 The early problems 490
16.2 Sociology of orthodox statistics 492
16.3 Ronald Fisher, Harold Jeffreys, and Jerzy Neyman 493
16.4 Pre-data and post-data considerations 499
16.5 The sampling distribution for an estimator 500
16.6 Pro-causal and anti-causal bias 503
16.7 What is real, the probability or the phenomenon? 505
16.8 Comments 506
16.8.1 Communication difficulties 507
17 Principles and pathology of orthodox statistics 509
17.1 Information loss 510
17.2 Unbiased estimators 511
17.3 Pathology of an unbiased estimate 516
17.4 The fundamental inequality of the sampling variance 518
17.5 Periodicity: the weather in Central Park 520
17.5.1 The folly of pre-filtering data 521
17.6 A Bayesian analysis 527
17.7 The folly of randomization 531
17.8 Fisher: common sense at Rothamsted 532
17.8.1 The Bayesian safety device 532
17.9 Missing data 533
17.10 Trend and seasonality in time series 534
17.10.1 Orthodox methods 535
17.10.2 The Bayesian method 536
17.10.3 Comparison of Bayesian and orthodox estimates 540
17.10.4 An improved orthodox estimate 541
17.10.5 The orthodox criterion of performance 544
17.11 The general case 545
17.12 Comments 550
18 The Ap distribution and rule of succession 553
18.1 Memory storage for old robots 553
18.2 Relevance 555
18.3 A surprising consequence 557
18.4 Outer and inner robots 55918.5 An application 561
18.6 Laplace’s rule of succession 563
18.7 Jeffreys’ objection 566
18.8 Bass or carp? 567
18.9 So where does this leave the rule? 568
18.10 Generalization 568
18.11 Confirmation and weight of evidence 571
18.11.1 Is indifference based on knowledge or ignorance? 573
18.12 Carnap’s inductive methods 574
18.13 Probability and frequency in exchangeable sequences 576
18.14 Prediction of frequencies 576
18.15 One-dimensional neutron multiplication 579
18.15.1 The frequentist solution 579
18.15.2 The Laplace solution 581
18.16 The de Finetti theorem 586
18.17 Comments 588
19 Physical measurements 589
19.1 Reduction of equations of condition 589
19.2 Reformulation as a decision problem 592
19.2.1 Sermon on Gaussian error distributions 592
19.3 The underdetermined case: K is singular 594
19.4 The overdetermined case: K can be made nonsingular 595
19.5 Numerical evaluation of the result 596
19.6 Accuracy of the estimates 597
19.7 Comments 599
19.7.1 A paradox 599
20 Model comparison 601
20.1 Formulation of the problem 602
20.2 The fair judge and the cruel realist 603
20.2.1 Parameters known in advance 604
20.2.2 Parameters unknown 604
20.3 But where is the idea of simplicity? 605
20.4 An example: linear response models 607
20.4.1 Digression: the old sermon still another time 608
20.5 Comments 613
20.5.1 Final causes 614
21 Outliers and robustness 615
21.1 The experimenter’s dilemma 615
21.2 Robustness 617
21.3 The two-model model 619
21.4 Exchangeable selection 620
21.5 The general Bayesian solution 622
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