Uncertainty : the soul of modeling, probability & statistics


    • Briggs, William


Uncertainty : the soul of modeling, probability & statistics

William Briggs

Springer, c2016

大学図書館所蔵 件 / 8



Includes bibliographical references (p. 245-252) and index



This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance." The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields - probability, physics, biology, the "soft" sciences, computer science - because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling.


1. Truth, Argument, Realism 1.1. Truth 1.2. Realism 1.3. Epistemology 1.4. Necessary & Conditional Truth 1.5. Science & Scientism 1.6. Faith 1.7. Belief & Knowlege 2. Logic 2.1. Language 2.2. Logic Is Not Empirical 2.3. Syllogistic Logic 2.4. Syllogisms 2.5. Informality 2.6. Fallacy 3. Induction and Intellection 3.1. Metaphysics 3.2. Types of Induction 3.3. Grue 4. What Probability Is 4.1. Probability Is Conditional 4.2. Relevance 4.3. The Proportional Syllogism 4.4. Details 4.5. Assigning Probability 4.6. Weight of Probability 4.7. Probability Usually Is Not a Number 4.8. Probability Can Be a Number 5. What Probability Is Not 5.1. Probability Is Not Physical 5.2. Probability & Essence 5.3. Probability Is Not Subjective 5.4. Probability Is Not Only Relative Frequency 5.5. Probability Is Not Always a Number Redux 6. Chance and Randomness 6.1. Randomness 6.2. Not a Cause 6.3. Experimental Design & Randomization 6.4. Nothing Is Distributed 6.5. Quantum Mechanics 6.6. Simulations 6.7. Truly Random & Information Theory 7. Causality 7.1. What Is Cause Like? 7.2. Causal Models 7.3. Paths 7.4. Once a Cause, Always a Cause 7.5. Falsifiability 7.6. Explanation 7.7. Under-Determination 8. Probability Models 8.1. Model Form 8.2. Relevance & Importance 8.3. Independence versus Irrelevance 8.4. Bayes 8.5. The Problem and Origin of Parameters 8.6. Exchangeability and Parameters 8.7. Mystery of Parameters 9. Statistical and Physical Models <9.1. The Idea 9.2. The Best Model 9.3. Second-Best Models 9.4. Relevance and Importance 9.5. Measurement 9.6. Hypothesis Testing 9.7. Die, P-Value, Die, Die, Die 9.8. Implementing Statistical Models 9.9. Model Goodness 9.10. Decisions 10. Modeling Goals, Strategies, and Mistakes 10.1. Regression 10.2. Risk 10.3. Epidemiologist Fallacy 10.4. Quantifying the Unquantifiable 10.5. Time Series10.6. The Future

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  • ISBN
    • 9783319397559
  • LCCN
  • 出版国コード
  • タイトル言語コード
  • 本文言語コード
  • 出版地
  • ページ数/冊数
    xix, 258 p.
  • 大きさ
    25 cm