Bayesian Confirmation Theory and The Likelihood Principle

The likelihood principle (LP) is a core issue in disagreements between Bayesian and frequentist statistical theories. Yet statements of the LP are often ambiguous, while arguments for why a Bayesian must accept it rely upon unexamined implicit premises. I distinguish two propositions associated with the LP, which I label LP1 and LP2. I maintain that there is a compelling Bayesian argument for LP1, based upon strict conditionalization, standard Bayesian decision theory, and a proposition I call the practical relevance principle. In contrast, I argue that there is no similarly compelling argument for or against LP2. I suggest that these conclusions lead to a restrictedly pluralistic view of Bayesian confirmation measures.

[1]  Branden Fitelson,et al.  Discussion: Re‐solving Irrelevant Conjunction with Probabilistic Independence* , 2004, Philosophy of Science.

[2]  J. Earman,et al.  Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory , 1994 .

[3]  P. Maher Subjective and Objective Confirmation , 1996, Philosophy of Science.

[4]  Deborah G. Mayo,et al.  Error and the Growth of Experimental Knowledge , 1996 .

[5]  P. Maher Betting on Theories , 1993 .

[6]  E. Eells,et al.  Symmetries and Asymmetries in Evidential Support , 2002 .

[7]  S. James Press,et al.  Subjective and Objective Bayesian Statistics , 2002 .

[8]  Branden Fitelson The Plurality of Bayesian Measures of Confirmation and the Problem of Measure Sensitivity , 1999, Philosophy of Science.

[9]  G. Wetherill,et al.  Comparative Statistical Inference , 1974, Technometrics.

[10]  Andrew Backe The Likelihood Principle and the Reliability of Experiments , 1999, Philosophy of Science.

[11]  Joseph B. Kadane,et al.  Reasoning to a foregone conclusion , 1996 .

[12]  Geoffrey Gregory,et al.  Foundations of Statistical Inference , 1973 .

[13]  C. D. Litton,et al.  Bayesian Approach to Interpreting Archaeological Data , 1996 .

[14]  R. Royall Statistical Evidence: A Likelihood Paradigm , 1997 .

[15]  James M. Joyce The Foundations of Causal Decision Theory by James M. Joyce , 1999 .

[16]  Vic Barnett,et al.  Comparative Statistical Inference , 1974, Technometrics.

[17]  James M. Joyce The Foundations of Causal Decision Theory , 1999 .

[18]  David Lindley,et al.  Logical foundations of probability , 1951 .

[19]  P. Maher,et al.  Bayesianism and Irrelevant Conjunction* , 2004, Philosophy of Science.

[20]  Branden Fitelson,et al.  A Bayesian Account of Independent Evidence with Applications , 2001, Philosophy of Science.

[21]  L. G. Neuberg,et al.  Bayes or Bust?-A Critical Examination of Bayesian Confirmation Theory. , 1994 .

[22]  Ian Hacking Logic of Statistical Inference , 1965 .

[23]  Rudolf Carnap,et al.  Logical foundations of probability , 1951 .

[24]  Elliott Sober,et al.  Epistemology for empiricists , 1993 .

[25]  Peter Urbach,et al.  Scientific Reasoning: The Bayesian Approach , 1989 .

[26]  S. Gupta,et al.  Statistical decision theory and related topics IV , 1988 .

[27]  G. Schlesinger Measuring degrees of confirmation , 1995 .

[28]  Branden Fitelson Putting the Irrelevance Back Into the Problem of Irrelevant Conjunction , 2002, Philosophy of Science.

[29]  Joseph B. Kadane,et al.  When Several Bayesians Agree That There Will Be No Reasoning to a Foregone Conclusion , 1996, Philosophy of Science.

[30]  R. Wolpert,et al.  Likelihood Principle , 2022, The SAGE Encyclopedia of Research Design.

[31]  C. Howson,et al.  Scientific Reasoning: The Bayesian Approach , 1989 .

[32]  P. Maher Inductive Logic and the Ravens Paradox , 1999, Philosophy of Science.

[33]  Daniel Steel,et al.  A Bayesian Way to Make Stopping Rules Matter , 2003 .

[34]  E. Eells,et al.  Measuring Confirmation and Evidence , 2000 .

[35]  Leonard J. Savage,et al.  The foundations of statistical inference : a discussion , 1962 .

[36]  Joseph B. Kadane,et al.  What is the Likelihood Function , 1988 .

[37]  Peter W. Milne log[P(h/eb)/P(h/b)] Is the One True Measure of Confirmation , 1996, Philosophy of Science.