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pro vyhledávání: '"Carrara, Nicholas"'
We describe a set of novel methods for efficiently sampling high-dimensional parameter spaces of physical theories defined at high energies, but constrained by experimental measurements made at lower energies. Often, theoretical models such as supers
Externí odkaz:
http://arxiv.org/abs/2305.12225
Autor:
Carrara, Nicholas
The following three sections and appendices are taken from my thesis "The Foundations of Inference and its Application to Fundamental Physics" from 2021, in which I construct a theory of entropic inference from first principles. The majority of these
Externí odkaz:
http://arxiv.org/abs/2207.08785
Autor:
Caticha, Ariel, Carrara, Nicholas
In the Entropic Dynamics (ED) approach the essence of quantum theory lies in its probabilistic nature while the Hilbert space structure plays a secondary and ultimately optional role. The dynamics of probability distributions is driven by the maximiz
Externí odkaz:
http://arxiv.org/abs/2007.15719
Autor:
Carrara, Nicholas, Ernst, Jesse
Publikováno v:
Presented at MaxEnt 2019, the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (June 30-- July 5, 2019)
In this paper we focus on the estimation of mutual information from finite samples $(\mathcal{X}\times\mathcal{Y})$. The main concern with estimations of mutual information is their robustness under the class of transformations for which it remains i
Externí odkaz:
http://arxiv.org/abs/1910.00365
Autor:
Carrara, Nicholas, Vanslette, Kevin
Publikováno v:
Entropy 2020, 22(3), 357
Using first principles from inference, we design a set of functionals for the purposes of \textit{ranking} joint probability distributions with respect to their correlations. Starting with a general functional, we impose its desired behaviour through
Externí odkaz:
http://arxiv.org/abs/1907.06992
Autor:
Carrara, Nicholas
Entropic Dynamics is a framework for deriving the laws of physics from entropic inference. In an (ED) of particles, the central assumption is that particles have definite yet unknown positions. By appealing to certain symmetries, one can derive a qua
Externí odkaz:
http://arxiv.org/abs/1907.00361
Autor:
Carrara, Nicholas, Ernst, Jesse A.
We propose an approach to rapidly find the upper limit of separability between datasets that is directly applicable to HEP classification problems. The most common HEP classification task is to use $n$ values (variables) for an object (event) to esti
Externí odkaz:
http://arxiv.org/abs/1708.09449
Autor:
Carrara, Nicholas, Caticha, Ariel
In the Entropic Dynamics framework the dynamics is driven by maximizing entropy subject to appropriate constraints. In this work we bring Entropic Dynamics one step closer to full equivalence with quantum theory by identifying constraints that lead t
Externí odkaz:
http://arxiv.org/abs/1708.08977
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