Husserl's Logic of Probability
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Philosophy of Probability and Statistical Modelling
PHILOSOPHY OF PROBABILITY AND STATISTICAL MODELLING Cambridge Elements, Cambridge University Press. ISBN 978-1-108-98494-2 (paperback) ISSN 2517-7273 (online) ISSN 2517-7265 (print) Mauricio Suárez – Complutense University of Madrid [email protected] 29,973 words Abstract: This book has two main aims. The first one (chapters 1-7) is an historically informed review of the philosophy of probability. It describes recent historiography, lays out the distinction between subjective and objective notions, and concludes by applying the historical lessons to the main interpretations of probability. The second aim (chapters 8-13) focuses entirely on objective probability, and advances a number of novel theses regarding its role in scientific practice. A distinction is drawn between traditional attempts to interpret chance, and a novel methodological study of its application. A radical form of pluralism is then introduced, advocating a tripartite distinction between propensities, probabilities and frequencies. Finally, a distinction is drawn between two different applications of chance in statistical modelling which, it is argued, vindicates the overall methodological approach. The ensuing conception of objective probability in practice is the ‘complex nexus of chance’. 1 Contents: Acknowledgements Introduction 1. The Archaeology of Probability 2. The Classical Interpretation: Equipossibility 3. The Logical Interpretation: Indifference 4. The Subjective Interpretation: Credence 5. The Reality of Chance: Empiricism and Pragmatism 6. The Frequency Interpretation: Actual and Hypothetical Frequencies 7. The Propensity Interpretation: Single Case and Long Run 8. Interpreting and Applying Objective Probability 9. The Explanatory Argument and Ontology 10. Metaphysical Pluralism: The Tripartite Conception 11. Methodological Pragmatism: The ‘Complex Nexus of Chance’ 12. -
The Untenability of a Priori Prior Probabilities in Objective Bayesian Conditionalization
Running Head: OBJECTIVE BAYESIAN CONDITIONALIZATION 1 The Untenability of A Priori Prior Probabilities in Objective Bayesian Conditionalization Chris Arledge A Senior Thesis submitted in partial fulfillment of the requirements for graduation in the Honors Program Liberty University Spring 2013 2 OBJECTVE BAYESIAN CONDITIONALIZATION Acceptance of Senior Honors Thesis This Senior Honors Thesis is accepted in partial fulfillment of the requirements for graduation from the Honors Program of Liberty University. ______________________________ Thomas Provenzola, Ph.D. Thesis Chair ____________________________ Edward Martin, Ph.D. Committee Member ______________________________ Evangelos Skoumbourdis, Ph.D. Committee Member ______________________________ Brenda Ayres, Ph.D. Honors Director ______________________________ Date 3 OBJECTVE BAYESIAN CONDITIONALIZATION Table of Contents Preliminary Remarks ........................................................................................................ 5 A Quinean Argument Against A Priori Prior Probabilities .......................................... 9 Epistemic Issues Concerning A Priori Probability Distributions ............................... 17 Quantitative Difficulties Concerning A Priori Probability Distributions .................. 27 Problems of the Ontological Grounding of Objective A Priori Prior Probabilities in Propositions ...................................................................................................................... 31 Concluding Remarks ...................................................................................................... -
Lecture Notes for the Introduction to Probability Course
Lecture Notes for the Introduction to Probability Course Vladislav Kargin December 5, 2020 Contents 1 Combinatorial Probability and Basic Laws of Probabilistic Inference 4 1.1 What is randomness and what is probability?. 4 1.1.1 The classical definition of probability . 4 1.1.2 Kolmogorov’s Axiomatic Probability . 9 1.1.3 More about set theory and indicator functions . 12 1.2 Discrete Probability Models. 16 1.3 Sample-point method (Combinatorial probability) . 19 1.4 Conditional Probability and Independence . 29 1.4.1 Conditional probability . 30 1.4.2 Independence . 32 1.4.3 Law of total probability . 34 1.5 Bayes’ formula . 39 2 Discrete random variables and probability distributions. 44 2.1 Pmf and cdf . 44 2.2 Expected value . 49 2.2.1 Definition . 49 2.2.2 Expected value for a function of a r.v. 51 2.2.3 Properties of the expected value . 53 2.3 Variance and standard deviation . 55 2.3.1 Definition . 55 2.3.2 Properties. 57 1 2.4 The zoo of discrete random variables . 58 2.4.1 Binomial . 59 2.4.2 Geometric. 63 2.4.3 Negative binomial . 65 2.4.4 Hypergeometric . 68 2.4.5 Poisson . 70 2.5 Moments and moment-generating function . 75 2.6 Markov’s and Chebyshev’s inequalities . 80 3 Chapter 4: Continuous random variables. 84 3.1 Cdf and pdf . 84 3.2 Expected value and variance . 89 3.3 Quantiles . 91 3.4 The Uniform random variable . 92 3.5 The normal (or Gaussian) random variable.