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On the computability of conditional probability  [PDF]
Nathanael L. Ackerman,Cameron E. Freer,Daniel M. Roy
Mathematics , 2010,
Abstract: As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe even more complex probabilistic models and inference algorithms. What are the limits of mechanizing probabilistic inference? We investigate the computability of conditional probability, a fundamental notion in probability theory and a cornerstone of Bayesian statistics, and show that there are computable joint distributions with noncomputable conditional distributions, ruling out the prospect of general inference algorithms, even inefficient ones. Specifically, we construct a pair of computable random variables in the unit interval such that the conditional distribution of the first variable given the second encodes the halting problem. Nevertheless, probabilistic inference is possible in many common modeling settings, and we prove several results giving broadly applicable conditions under which conditional distributions are computable. In particular, conditional distributions become computable when measurements are corrupted by independent computable noise with a sufficiently smooth density.
Lexicographic probability, conditional probability, and nonstandard probability  [PDF]
Joseph Y. Halpern
Computer Science , 2003,
Abstract: The relationship between Popper spaces (conditional probability spaces that satisfy some regularity conditions), lexicographic probability systems (LPS's), and nonstandard probability spaces (NPS's) is considered. If countable additivity is assumed, Popper spaces and a subclass of LPS's are equivalent; without the assumption of countable additivity, the equivalence no longer holds. If the state space is finite, LPS's are equivalent to NPS's. However, if the state space is infinite, NPS's are shown to be more general than LPS's.
Logical, conditional, and classical probability  [PDF]
G. A. Quznetsov
Mathematics , 2005,
Abstract: The propositional logic is generalized on the real numbers field. the logical function with all properties of the classical probability function is obtained. The logical analog of the Bernoulli independent tests scheme is constructed. The logical analog of the Large Number Law is deduced from properties of these functions. The logical analog of thd conditional probability is defined. Consistency encured by a model on a suitable variant of the nonstandard analysis.
REGULAR CONDITIONAL PROBABILITY, DISINTEGRATION OF PROBABILITY AND RADON SPACES
LEAO Jr.,D.; FRAGOSO,M.; RUFFINO,P.;
Proyecciones (Antofagasta) , 2004, DOI: 10.4067/S0716-09172004000100002
Abstract: we establish equivalence of several regular conditional probability properties and radon space. in addition, we introduce the universally measurable disintegration concept and prove an existence result
REGULAR CONDITIONAL PROBABILITY, DISINTEGRATION OF PROBABILITY AND RADON SPACES  [cached]
D. LEAO Jr.,M. FRAGOSO,P. RUFFINO
Proyecciones (Antofagasta) , 2004,
Abstract: We establish equivalence of several regular conditional probability properties and Radon space. In addition, we introduce the universally measurable disintegration concept and prove an existence result
Conditional Kolmogorov Complexity and Universal Probability  [PDF]
Paul M. B. Vitanyi
Mathematics , 2012,
Abstract: The Coding Theorem of L.A. Levin connects unconditional prefix Kolmogorov complexity with the discrete universal distribution. There are conditional versions referred to in several publications but as yet there exist no written proofs in English. Here we provide those proofs. They use a different definition than the standard one for the conditional version of the discrete universal distribution. Under the classic definition of conditional probability, there is no conditional version of the Coding Theorem.
Knowledge Integration for Conditional Probability Assessments  [PDF]
Angelo Gilio,Fulvio Spezzaferri
Computer Science , 2013,
Abstract: In the probabilistic approach to uncertainty management the input knowledge is usually represented by means of some probability distributions. In this paper we assume that the input knowledge is given by two discrete conditional probability distributions, represented by two stochastic matrices P and Q. The consistency of the knowledge base is analyzed. Coherence conditions and explicit formulas for the extension to marginal distributions are obtained in some special cases.
Scaling conditional tail probability and quantile estimators  [PDF]
John Cotter
Quantitative Finance , 2011,
Abstract: We present a novel procedure for scaling relatively high frequency tail probability and quantile estimates for the conditional distribution of returns.
Quantum Dynamics as an analog of Conditional Probability  [PDF]
M. S. Leifer
Physics , 2006, DOI: 10.1103/PhysRevA.74.042310
Abstract: Quantum theory can be regarded as a non-commutative generalization of classical probability. From this point of view, one expects quantum dynamics to be analogous to classical conditional probabilities. In this paper, a variant of the well-known isomorphism between completely positive maps and bipartite density operators is derived, which makes this connection much more explicit. The new isomorphism is given an operational interpretation in terms of statistical correlations between ensemble preparation procedures and outcomes of measurements. Finally, the isomorphism is applied to elucidate the connection between no-cloning/no-broadcasting theorems and the monogamy of entanglement, and a simplified proof of the no-broadcasting theorem is obtained as a byproduct.
Assessing Students’ Difficulties with Conditional Probability and Bayesian Reasoning  [PDF]
Carmen Díaz,Inmaculada de la Fuente
International Electronic Journal of Mathematics Education , 2007,
Abstract: In this paper we first describe the process of building a questionnaire directed to globally assess formal understanding of conditional probability and the psychological biases related to this concept. We then present results from applying the questionnaire to a sample of 414 students, after they had been taught the topic. Finally, we use Factor Analysis to show that formal knowledge of conditional probability in these students was unrelated to the different biases in conditional probability reasoning. These biases also appeared unrelated in our sample. We conclude with some recommendations about how to improve the teaching of conditional probability.
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