Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Ried, M. Agnew, L. Vermeyden, D. Janzing, R. Spekkens, K. Resch (2015)
A quantum advantage for inferring causal structureNature Physics, 11
M. Leifer, M. Leifer, R. Spekkens (2011)
Towards a formulation of quantum theory as a causally neutral theory of Bayesian inferencePhysical Review A, 88
M. Leifer, R. Spekkens (2011)
A Bayesian approach to compatibility, improvement, and pooling of quantum statesJournal of Physics A: Mathematical and Theoretical, 47
S. Korotaev, E. Kiktenko (2010)
QUANTUM CAUSALITY
GM D’Ariano, RD Gill, M Keyl, B Kuemmerer, H Maassen, RF Werner (2002)
The quantum monty hall problemQuantum Inf. Comput., 2
P. Gawron (2009)
NOISY QUANTUM MONTY HALL GAMEFluctuation and Noise Letters, 09
N. Cerf, C. Adami (1995)
Negative entropy and information in quantum mechanicsPhysical Review Letters, 79
(1975)
A problem in probability (letter to the editor)
S Selvin (1975)
On the monty hall problem (letter to the editor)Am. Stat., 29
A. Flitney, D. Abbott (2001)
Quantum version of the Monty Hall problemPhysical Review A, 65
M. Leifer (2006)
Quantum dynamics as an analog of conditional probability (12 pages)Physical Review A, 74
D. Koller, N. Friedman (2009)
Probabilistic Graphical Models - Principles and Techniques
WhitfordAndrew (2014)
Bayesian Methods: A Social and Behavioral Sciences Approach
N. Cerf, C. Adami (1999)
Quantum extension of conditional probabilityPhysical Review A, 60
Chuan‐Feng Li, Yong-Sheng Zhang, Yun-Feng Huang, G. Guo (2000)
Quantum strategies of quantum measurementsPhysics Letters A, 280
N. Fenton, M. Neil (2012)
Risk Assessment and Decision Analysis with Bayesian Networks
Salman Khan, M. Ramzan, M. Khan (2009)
Quantum Monty Hall Problem under DecoherenceCommunications in Theoretical Physics, 54
M. Leifer, D. Poulin (2007)
Quantum Graphical Models and Belief PropagationAnnals of Physics, 323
MS Leifer (2006)
Quantum dynamics as an analog of conditional probabilityPhys. Rev. A, 74
Radford Neal (2006)
Pattern Recognition and Machine LearningPattern Recognition and Machine Learning
This paper presents a quantum version of the Monty Hall problem based upon the quantum inferring acausal structures, which can be identified with generalization of Bayesian networks. Considered structures are expressed in formalism of quantum information theory, where density operators are identified with quantum generalization of probability distributions. Conditional relations between quantum counterpart of random variables are described by quantum conditional operators. Presented quantum inferring structures are used to construct a model inspired by scenario of well-known Monty Hall game, where we show the differences between classical and quantum Bayesian reasoning.
Quantum Information Processing – Springer Journals
Published: Sep 8, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.