Cox's theorem

Cox's theorem, named after the physicist Richard Threlkeld Cox, is a derivation of the laws of probability theory from a certain set of postulates. This derivation justifies the so-called "logical" interpretation of probability. As the laws of probability derived by Cox's theorem are applicable to any proposition, logical probability is a type of Bayesian probability. Other forms of Bayesianism, such as the subjective interpretation, are given other justifications.

Cox's assumptions

Cox wanted his system to satisfy the following conditions:

  1. Divisibility and comparability – The plausibility of a statement is a real number and is dependent on information we have related to the statement.
  2. Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
  3. Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.

The postulates as stated here are taken from Arnborg and Sjödin.[1][2][3] "Common sense" includes consistency with Aristotelian logic when statements are completely plausible or implausible.

The postulates as originally stated by Cox were not mathematically rigorous (although better than the informal description above), e.g., as noted by Halpern.[4][5] However it appears to be possible to augment them with various mathematical assumptions made either implicitly or explicitly by Cox to produce a valid proof.

Cox's axioms and functional equations are:

f(f(x))=x,\,
saying that the function f that maps the probability of a proposition to the probability of the proposition's negation is an involution, i.e., it is its own inverse.
P(A\ \mbox{and}\ B)=g(P(A),P(B|A))
is an associative binary operation. All strictly increasing associative binary operations on the real numbers are isomorphic to multiplication of numbers in the interval [0, 1]. This function therefore may be taken to be multiplication.
y\,f\left({f(z) \over y}\right)=z\,f\left({f(y) \over z}\right)
where f is as above.

Cox's theorem implies that any plausibility model that meets the postulates is equivalent to the subjective probability model, i.e., can be converted to the probability model by rescaling.

Implications of Cox's postulates

The laws of probability derivable from these postulates are the following.[6] Here w(A|B) is the "plausibility" of the proposition A given B, and m is some positive number. Further, AC represents the absolute complement of A.

  1. Certainty is represented by w(A|B) = 1.
  2. For some real number m, wm(A|B) + wm(AC|B) = 1.
  3. w(A, B|C) = w(A|C) w(B|A, C) = w(B|C) w(A|B, C).

It is important to note that the postulates imply only these general properties. We may recover the usual laws of probability by setting a new function, conventionally denoted P or Pr, equal to wm. Then we obtain the laws of probability in a more familiar form:

  1. Certain truth is represented by Pr(A|B) = 1, and certain falsehood by Pr(A|B) = 0. (If m is negative, this corresponds to certain falsehood being represented by w(A|B) = infinity.)
  2. Pr(A|B) + Pr(AC|B) = 1
  3. Pr(A, B|C) = Pr(A|C) Pr(B|A, C) = Pr(B|C) Pr(A|B, C).

Rule 2 is a rule for negation, and rule 3 is a rule for conjunction. Given that any proposition containing conjunction, disjunction, and negation can be equivalently rephrased using conjunction and negation alone (the conjunctive normal form), we can now handle any compound proposition.

The laws thus derived yield finite additivity of probability, but not countable additivity. The measure-theoretic formulation of Kolmogorov assumes that a probability measure is countably additive. This slightly stronger condition is necessary for the proof of certain theorems.

Interpretation and further discussion

Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. For example, in Jaynes[6] it is discussed in detail in chapters 1 and 2 and is a cornerstone for the rest of the book. Probability is interpreted as a formal system of logic, the natural extension of Aristotelian logic (in which every statement is either true or false) into the realm of reasoning in the presence of uncertainty.

It has been debated to what degree the theorem excludes alternative models for reasoning about uncertainty. For example, if certain "unintuitive" mathematical assumptions were dropped then alternatives could be devised, e.g., an example provided by Halpern.[4] However Arnborg and Sjödin[1][2][3] suggest additional "common sense" postulates, which would allow the assumptions to be relaxed in some cases while still ruling out the Halpern example. Other approaches were devised by Hardy [7] or Dupré and Tipler.[8]

The original formulation of Cox's theorem is in Cox (1946) which is extended with additional results and more discussion in Cox (1961). Jaynes[6] cites Abel[9] for the first known use of the associativity functional equation. Aczél[10] provides a long proof of the "associativity equation" (pages 256-267). Jaynes [6](p27) reproduces the shorter proof by Cox in which differentiability is assumed. A guide to Cox's theorem by Van Horn aims at comprehensively introducing the reader to all these references.[11]

See also

References

  1. 1 2 Stefan Arnborg and Gunnar Sjödin, On the foundations of Bayesianism, Preprint: Nada, KTH (1999) — ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/06arnborg.ps — ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/06arnborg.pdf
  2. 1 2 Stefan Arnborg and Gunnar Sjödin, A note on the foundations of Bayesianism, Preprint: Nada, KTH (2000a) — ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobshle.ps — ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobshle.pdf
  3. 1 2 Stefan Arnborg and Gunnar Sjödin, "Bayes rules in finite models," in European Conference on Artificial Intelligence, Berlin, (2000b) — ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobc1.ps — ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobc1.pdf
  4. 1 2 Joseph Y. Halpern, "A counterexample to theorems of Cox and Fine," Journal of AI research, 10, 67–85 (1999) — http://www.jair.org/media/536/live-536-2054-jair.ps.Z
  5. ↑ Joseph Y. Halpern, "Technical Addendum, Cox's theorem Revisited," Journal of AI research, 11, 429–435 (1999) — http://www.jair.org/media/644/live-644-1840-jair.ps.Z
  6. 1 2 3 4 Edwin Thompson Jaynes, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
  7. ↑ Michael Hardy, "Scaled Boolean algebras", Advances in Applied Mathematics, August 2002, pages 243–292 (or preprint); Hardy has said, "I assert there that I think Cox's assumptions are too strong, although I don't really say why. I do say what I would replace them with." (The quote is from a Wikipedia discussion page, not from the article.)
  8. ↑ Dupré, Maurice J., Tipler, Frank J. New Axioms For Bayesian Probability, Bayesian Analysis (2009), Number 3, pp. 599-606
  9. ↑ Niels Henrik Abel "Untersuchung der Functionen zweier unabhängig veränderlichen Gröszen x und y, wie f(x, y), welche die Eigenschaft haben, dasz f[z, f(x,y)] eine symmetrische Function von z, x und y ist.", Jour. Reine u. angew. Math. (Crelle's Jour.), 1, 11–15, (1826).
  10. ↑ János Aczél, Lectures on Functional Equations and their Applications, Academic Press, New York, (1966).
  11. ↑ Van Horn, K. S. (2003). "Constructing a logic of plausible inference: A guide to Cox's theorem". International Journal of Approximate Reasoning 34: 3–24. doi:10.1016/S0888-613X(03)00051-3.
  • Cox, R. T. (1946). "Probability, Frequency and Reasonable Expectation". American Journal of Physics 14: 1–10. doi:10.1119/1.1990764. 
  • Cox, R. T. (1961). The Algebra of Probable Inference. Baltimore, MD: Johns Hopkins University Press. 
  • Terrence L. Fine, Theories of Probability; An examination of foundations, Academic Press, New York, (1973).
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