Academic literature on the topic 'Kolmogorov extension theorem'

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Journal articles on the topic "Kolmogorov extension theorem"

1

Tumulka, Roderich. "A Kolmogorov Extension Theorem for POVMs." Letters in Mathematical Physics 84, no. 1 (2008): 41–46. http://dx.doi.org/10.1007/s11005-008-0229-8.

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2

López-Gótnez, Angel, and Kaoru Hirota. "Fuzzification of Kolmogorov Theorem." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 2 (2001): 99–109. http://dx.doi.org/10.20965/jaciii.2001.p0099.

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The extension of the Kolmogorov’s theorem (1957) about the representation of continuous real extended functions with more than one variables as superpositions of extended functions of one variable is presented. The graphical analysis of its behavior with a function of two variables is showed. The representation of the theorem using a practical neural network is obtained.
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3

Stochel, Jan. "The Bochner-Kolmogorov extension theorem for semispectral measures." Colloquium Mathematicum 54, no. 1 (1987): 83–94. http://dx.doi.org/10.4064/cm-54-1-83-94.

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4

Milz, Simon, Fattah Sakuldee, Felix A. Pollock, and Kavan Modi. "Kolmogorov extension theorem for (quantum) causal modelling and general probabilistic theories." Quantum 4 (April 20, 2020): 255. http://dx.doi.org/10.22331/q-2020-04-20-255.

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In classical physics, the Kolmogorov extension theorem lays the foundation for the theory of stochastic processes. It has been known for a long time that, in its original form, this theorem does not hold in quantum mechanics. More generally, it does not hold in any theory of stochastic processes -- classical, quantum or beyond -- that does not just describe passive observations, but allows for active interventions. Such processes form the basis of the study of causal modelling across the sciences, including in the quantum domain. To date, these frameworks have lacked a conceptual underpinning similar to that provided by Kolmogorov’s theorem for classical stochastic processes. We prove a generalized extension theorem that applies to all theories of stochastic processes, putting them on equally firm mathematical ground as their classical counterpart. Additionally, we show that quantum causal modelling and quantum stochastic processes are equivalent. This provides the correct framework for the description of experiments involving continuous control, which play a crucial role in the development of quantum technologies. Furthermore, we show that the original extension theorem follows from the generalized one in the correct limit, and elucidate how a comprehensive understanding of general stochastic processes allows one to unambiguously define the distinction between those that are classical and those that are quantum.
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5

Chen, Zengjing, and Feng Hu. "A law of the iterated logarithm under sublinear expectations." Journal of Financial Engineering 01, no. 02 (2014): 1450015. http://dx.doi.org/10.1142/s2345768614500159.

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In this paper, with the notion of independent and identically distributed (IID) random variables under sublinear expectations initiated by Peng, we develop a law of the iterated logarithm (LIL) for capacities. It turns out that our theorem is a natural extension of the Kolmogorov and the Hartman–Wintner LIL.
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6

Gomez, Ignacio S. "An Estimation of the Logarithmic Timescale in Ergodic Dynamics." International Journal of Bifurcation and Chaos 28, no. 01 (2018): 1850002. http://dx.doi.org/10.1142/s0218127418500025.

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An estimation of the logarithmic timescale in quantum systems having an ergodic dynamics in the semiclassical limit, is presented. The estimation is based on an extension of the Krieger’s finite generator theorem for discretized [Formula: see text]-algebras and using the time rescaling property of the Kolmogorov–Sinai entropy. The results are in agreement with those obtained in the literature but with a simpler mathematics and within the context of the ergodic theory. Moreover, some consequences of the Poincaré’s recurrence theorem are also explored.
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7

Gerhard, Felipe, Robert Haslinger, and Gordon Pipa. "Applying the Multivariate Time-Rescaling Theorem to Neural Population Models." Neural Computation 23, no. 6 (2011): 1452–83. http://dx.doi.org/10.1162/neco_a_00126.

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Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However, any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based on the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models that neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem and provide a practical step-by-step procedure for applying it to testing the sufficiency of neural population models. Using several simple analytically tractable models and more complex simulated and real data sets, we demonstrate that important features of the population activity can be detected only using the multivariate extension of the test.
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8

Fritz, Tobias, and Eigil Fjeldgren Rischel. "Infinite products and zero-one laws in categorical probability." Compositionality 2 (August 11, 2020): 3. http://dx.doi.org/10.32408/compositionality-2-3.

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Markov categories are a recent category-theoretic approach to the foundations of probability and statistics. Here we develop this approach further by treating infinite products and the Kolmogorov extension theorem. This is relevant for all aspects of probability theory in which infinitely many random variables appear at a time. These infinite tensor products ⨂i∈JXi come in two versions: a weaker but more general one for families of objects (Xi)i∈J in semicartesian symmetric monoidal categories, and a stronger but more specific one for families of objects in Markov categories.As a first application, we state and prove versions of the zero--one laws of Kolmogorov and Hewitt--Savage for Markov categories. This gives general versions of these results which can be instantiated not only in measure-theoretic probability, where they specialize to the standard ones in the setting of standard Borel spaces, but also in other contexts.
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9

Si, Congfang, Yuanguang Wang, Yixin Zhang, Jianyu Wang, and Jianjun Jia. "Extension of the van Cittert–Zernike theorem for completely polarized incoherent electromagnetic beam propagating through non-Kolmogorov turbulence." Optik 122, no. 21 (2011): 1922–26. http://dx.doi.org/10.1016/j.ijleo.2010.12.006.

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10

HUTTER, MARCUS. "THE FASTEST AND SHORTEST ALGORITHM FOR ALL WELL-DEFINED PROBLEMS." International Journal of Foundations of Computer Science 13, no. 03 (2002): 431–43. http://dx.doi.org/10.1142/s0129054102001199.

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An algorithm M is described that solves any well-defined problem p as quickly a the fastest algorithm computing a solution to p, save for a factor of 5 and low-order additive terms. M optimally distributes resources between the execution of provably correct p-solving programs and an enumeration of all proofs, including relevant proofs of program correctness and of time bounds on program runtimes. M avoids Blum's speed-up theorem by ignoring programs without correctness proof. M has broader applicability and can be faster than Levin's universal search, the fastest method for inverting functions save for a large multiplicative constant. An extension of Kolmogorov complexity and two novel natural measures of function complexity are used to show the most efficient program computing some function f is also among the shortest programs provably computing f.
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