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How strongly should you believe the various propositions that you can express?
That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent's evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms
· Probability - degrees of belief should be probabilities
· Calibration - they should be calibrated with evidence
· Equivocation - they should otherwise equivocate between basic outcomes
Objective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failure to learn from experience. It has also been accused of being computationally intractable, susceptible to paradox, language dependent, and of not being objective enough.
Especially suitable for graduates or researchers in philosophy of science, foundations of statistics and artificial intelligence, the book argues that these criticisms can be met and that objective Bayesianism is a promising theory with an exciting agenda for further research.
ebook,Jon Williamson,In Defence of Objective Bayesianism,OUP Oxford,Bayesian statistical decision theory,Bayesian statistical decision theory.,Great Britain/British Isles,Knowledge, Theory of,Knowledge, Theory of.,Logic,MATHEMATICS / Logic,MATHEMATICS / Probability Statistics / Bayesian Analysis,Mathematical logic,Mathematical models,Mathematics,Mathematics | Logic,Mathematics/Probability Statistics - Bayesian Analysis,Non-Fiction,Probability Statistics - Bayesian Analysis,Reasoning,Reasoning - Mathematical models,Reasoning;Mathematical models.,Scholarly/Graduate,Science/Math,Science/Mathematics,UNIVERSITY PRESS,Logic,MATHEMATICS / Logic,MATHEMATICS / Probability Statistics / Bayesian Analysis,Mathematics/Probability Statistics - Bayesian Analysis,Probability Statistics - Bayesian Analysis,Mathematics,Mathematical models,Reasoning,Science/Mathematics,Philosophy logic,Probability statistics,Artificial intelligence
In Defence of Objective Bayesianism 1 Jon Williamson Reviews :
That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent's evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms
· Probability - degrees of belief should be probabilities
· Calibration - they should be calibrated with evidence
· Equivocation - they should otherwise equivocate between basic outcomes
Objective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failure to learn from experience. It has also been accused of being computationally intractable, susceptible to paradox, language dependent, and of not being objective enough.
Especially suitable for graduates or researchers in philosophy of science, foundations of statistics and artificial intelligence, the book argues that these criticisms can be met and that objective Bayesianism is a promising theory with an exciting agenda for further research.
ebook,Jon Williamson,In Defence of Objective Bayesianism,OUP Oxford,Bayesian statistical decision theory,Bayesian statistical decision theory.,Great Britain/British Isles,Knowledge, Theory of,Knowledge, Theory of.,Logic,MATHEMATICS / Logic,MATHEMATICS / Probability Statistics / Bayesian Analysis,Mathematical logic,Mathematical models,Mathematics,Mathematics | Logic,Mathematics/Probability Statistics - Bayesian Analysis,Non-Fiction,Probability Statistics - Bayesian Analysis,Reasoning,Reasoning - Mathematical models,Reasoning;Mathematical models.,Scholarly/Graduate,Science/Math,Science/Mathematics,UNIVERSITY PRESS,Logic,MATHEMATICS / Logic,MATHEMATICS / Probability Statistics / Bayesian Analysis,Mathematics/Probability Statistics - Bayesian Analysis,Probability Statistics - Bayesian Analysis,Mathematics,Mathematical models,Reasoning,Science/Mathematics,Philosophy logic,Probability statistics,Artificial intelligence
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