Carl G Wagner
Dados Biográficos
| ID | 3874167 |
|---|---|
| NOME | Carl G Wagner |
| PRENOMES | Carl G |
| SOBRENOME | Wagner |
| ASSINATURA | WAGNER C G |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 8 |
| TOTAL DE CITAÇÕES | 22 |
| TOTAL COMO AUTOR | 8 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 1997 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2024 |
| ÍNDICE H | 2 |
Recovering a Prior from a Posterior
Postscript to Richard Jeffrey’s “Conditioning, Kinematics, and Exchangeability”
Richard Jeffrey’s “Conditioning, Kinematics, and Exchangeability” is one of the foundational documents of probability kinematics. However, the section entitled “Successive Updating” contains a subtle error regarding the applicability of updating by so-called relevance quotients in order to ensure the commutativity of successive probability kinematical revisions. Upon becoming aware of this error, Jeffrey formulated the appropriate remedy, but he …
The corroboration paradox
Modus Tollens Probabilized
We establish a probabilized version of modus tollens, deriving from p(E|H)=a and p(Ē)=b the best possible bounds on p(H̄). In particular, we show that p(H̄) → 1 as a, b → 1, and also as a, b → 0. 1. Introduction 2. Probabilities of conditionals 3. Conditional probabilities 3.1 Adams' thesis 3.2 Modus ponens for conditional probabilities 3.3 Modus tollens for conditional probabilities
Probability Kinematics and Commutativity
The so-called “non-commutativity” of probability kinematics has caused much unjustified concern. When identical learning is properly represented, namely, by identical Bayes factors rather than identical posterior probabilities, then sequential probability-kinematical revisions behave just as they should. Our analysis is based on a variant of Field's reformulation of probability kinematics, divested of its (inessential) physicalist gloss
Old Evidence and New Explanation III
Garber (1983) and Jeffrey (1991, 1995) have both proposed solutions to the old evidence problem. Jeffrey's solution, based on a new probability revision method called reparation , has been generalized to the case of uncertain old evidence and probabilistic new explanation in Wagner 1997, 1999. The present paper reformulates some of the latter work, highlighting the central role of Bayes factors and their associated uniformity principle , and exte…
Old Evidence and New Explanation II
Additional results are reported on the author's earlier generalization of Richard Jeffrey's solution to the problem of old evidence and new explanation
Old Evidence and New Explanation
Jeffrey has devised a probability revision method that increases the probability of hypothesis H when it is discovered that H implies previously known evidence E. A natural extension of Jeffrey's method likewise increases the probability of H when E has been established with sufficiently high probability and it is then discovered, quite apart from this, that H confers sufficiently higher probability on E than does its logical negation H̄
Probability Kinematics and Commutativity
The so-called “non-commutativity” of probability kinematics has caused much unjustified concern. When identical learning is properly represented, namely, by identical Bayes factors rather than identical posterior probabilities, then sequential probability-kinematical revisions behave just as they should. Our analysis is based on a variant of Field's reformulation of probability kinematics, divested of its (inessential) physicalist gloss
Modus Tollens Probabilized
We establish a probabilized version of modus tollens, deriving from p(E|H)=a and p(Ē)=b the best possible bounds on p(H̄). In particular, we show that p(H̄) → 1 as a, b → 1, and also as a, b → 0. 1. Introduction 2. Probabilities of conditionals 3. Conditional probabilities 3.1 Adams' thesis 3.2 Modus ponens for conditional probabilities 3.3 Modus tollens for conditional probabilities
Old Evidence and New Explanation
Jeffrey has devised a probability revision method that increases the probability of hypothesis H when it is discovered that H implies previously known evidence E. A natural extension of Jeffrey's method likewise increases the probability of H when E has been established with sufficiently high probability and it is then discovered, quite apart from this, that H confers sufficiently higher probability on E than does its logical negation H̄
Old Evidence and New Explanation III
Garber (1983) and Jeffrey (1991, 1995) have both proposed solutions to the old evidence problem. Jeffrey's solution, based on a new probability revision method called reparation , has been generalized to the case of uncertain old evidence and probabilistic new explanation in Wagner 1997, 1999. The present paper reformulates some of the latter work, highlighting the central role of Bayes factors and their associated uniformity principle , and exte…
Old Evidence and New Explanation II
Additional results are reported on the author's earlier generalization of Richard Jeffrey's solution to the problem of old evidence and new explanation
Old Evidence and New Explanation
Jeffrey has devised a probability revision method that increases the probability of hypothesis H when it is discovered that H implies previously known evidence E. A natural extension of Jeffrey's method likewise increases the probability of H when E has been established with sufficiently high probability and it is then discovered, quite apart from this, that H confers sufficiently higher probability on E than does its logical negation H̄
Old Evidence and New Explanation II
Additional results are reported on the author's earlier generalization of Richard Jeffrey's solution to the problem of old evidence and new explanation
Old Evidence and New Explanation III
Garber (1983) and Jeffrey (1991, 1995) have both proposed solutions to the old evidence problem. Jeffrey's solution, based on a new probability revision method called reparation , has been generalized to the case of uncertain old evidence and probabilistic new explanation in Wagner 1997, 1999. The present paper reformulates some of the latter work, highlighting the central role of Bayes factors and their associated uniformity principle , and exte…
Probability Kinematics and Commutativity
The so-called “non-commutativity” of probability kinematics has caused much unjustified concern. When identical learning is properly represented, namely, by identical Bayes factors rather than identical posterior probabilities, then sequential probability-kinematical revisions behave just as they should. Our analysis is based on a variant of Field's reformulation of probability kinematics, divested of its (inessential) physicalist gloss
Modus Tollens Probabilized
We establish a probabilized version of modus tollens, deriving from p(E|H)=a and p(Ē)=b the best possible bounds on p(H̄). In particular, we show that p(H̄) → 1 as a, b → 1, and also as a, b → 0. 1. Introduction 2. Probabilities of conditionals 3. Conditional probabilities 3.1 Adams' thesis 3.2 Modus ponens for conditional probabilities 3.3 Modus tollens for conditional probabilities
The corroboration paradox
Postscript to Richard Jeffrey’s “Conditioning, Kinematics, and Exchangeability”
Richard Jeffrey’s “Conditioning, Kinematics, and Exchangeability” is one of the foundational documents of probability kinematics. However, the section entitled “Successive Updating” contains a subtle error regarding the applicability of updating by so-called relevance quotients in order to ensure the commutativity of successive probability kinematical revisions. Upon becoming aware of this error, Jeffrey formulated the appropriate remedy, but he …
Recovering a Prior from a Posterior
Mathematics (7 obras) · Computer Science (6 obras) · Philosophy (6 obras) · Epistemology (5 obras) · Mathematical economics (5 obras) · Statistics (5 obras) · Bayesian Modeling and Causal Inference (4 obras) · Philosophy and History of Science (4 obras) · Bayes' theorem (2 obras) · Bayesian probability (2 obras)