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A growing body of research on probabilistic programs and causal models has highlighted the need to reason compositionally about model classes that extend directed graphical models. Both probabilistic programs and causal models define a…

Programming Languages · Computer Science 2023-12-15 Eli Sennesh , Jan-Willem van de Meent

Recent developments in the formalisation of quantum causal structures have made it possible to test and compare hypotheses about causal structure empirically, rather than being a-priori assumptions. Such differences in causal structure may…

Quantum Physics · Physics 2026-05-28 Declan Maguire , Fabio Costa

We develop coarse-graining schemes for stochastic many-particle microscopic models with competing short- and long-range interactions on a d-dimensional lattice. We focus on the coarse-graining of equilibrium Gibbs states and using cluster…

Numerical Analysis · Mathematics 2010-03-09 M. A. Katsoulakis , P. Plechac , L. Rey-Bellet , D. K. Tsagkarogiannis

We analyze the notion that physical theories are quantitative and testable by observations in experiments. This leads us to propose a new, Bayesian, interpretation of probabilities in physics that unifies their current use in classical…

Quantum Physics · Physics 2007-05-23 Francis G. Perey

We present conditions every measure of entanglement has to satisfy and construct a whole class of 'good' entanglement measures. The generalization of our class of entanglement measures to more than two particles is straightforward. We…

Quantum Physics · Physics 2009-01-23 V. Vedral , M. B. Plenio , M. A. Rippin , P. L. Knight

We introduce the boolean convolution for probability measures on the unit circle. Roughly speaking, it describes the distribution of the product of two boolean independent unitary random variables. We find an analogue of the characteristic…

Functional Analysis · Mathematics 2009-06-13 Uwe Franz

We present a general framework for uncertainty quantification that is a mosaic of interconnected models. We define global first and second order structural and correlative sensitivity analyses for random counting measures acting on risk…

Probability · Mathematics 2021-01-05 Caleb Deen Bastian , Herschel Rabitz

We develop a pseudo-metric analogue of bisimulation for generalized semi-Markov processes. The kernel of this pseudo-metric corresponds to bisimulation; thus we have extended bisimulation for continuous-time probabilistic processes to a…

Logic in Computer Science · Computer Science 2017-01-11 Vineet Gupta , Radha Jagadeesan , Prakash Panangaden

This work derives closed-form expressions computing the expectation of co-presence and of number of co-occurrences of nodes on paths sampled from a network according to general path weights (a bag of paths). The underlying idea is that two…

Machine Learning · Computer Science 2021-08-24 Guillaume Guex , Sylvain Courtain , Marco Saerens

We propose a general experimental scheme based on binary trees of partially polarizing beam splitters (PPBSs) for realizing sequential unsharp measurements of photon polarization. The sharpnesses and the bases of the particular photon…

Quantum Physics · Physics 2024-10-23 Peter Adam , Lajos Diósi

Various forms of optimality for quantum observables described as normalized positive operator valued measures (POVMs) are studied in this paper. We give characterizations for observables that determine the values of the measured quantity…

Quantum Physics · Physics 2018-09-28 Erkka Haapasalo , Juha-Pekka Pellonpaa

Introduction to the theory of decoherence. Contents: 1. The phenomenon of decoherence: superpositions, superselection rules, decoherence by "measurements". 2. Observables as a derivable concept. 3. The measurement problem. 4. Density…

Quantum Physics · Physics 2008-02-03 H. D. Zeh

We introduce an RG-inspired coarse-graining for extracting the collective features of data. The key to successful coarse-graining lies in finding appropriate pairs of data sets. We coarse-grain the two closest data in a regular real-space…

Data Analysis, Statistics and Probability · Physics 2023-07-19 Jonathan Landy , Tsvi Tlusty , YeongKyu Lee , YongSeok Jho

A number of complexity measures for Boolean functions have previously been introduced. These include (1) sensitivity, (2) block sensitivity, (3) witness complexity, (4) subcube partition complexity and (5) algorithmic complexity. Each of…

Probability · Mathematics 2024-08-26 Laurin Köhler-Schindler , Jeffrey E. Steif

We have made the first experimental demonstration of the simultaneous minimum uncertainty product between two complementary observables for a two-state system (a qubit). A partially entangled two-photon state was used to perform such…

Quantum Physics · Physics 2007-05-23 Alexei Trifonov , Gunnar Bjork , Jonas Soderholm

Quantization of a probability measure means representing it with a finite set of Dirac masses that approximates the input distribution well enough (in some metric space of probability measures). Various methods exists to do so, but the…

Machine Learning · Statistics 2024-02-12 Gabriel Turinici

The properties of interstellar grains can now be defined by a rapidly growing wealth of observational data. We rely upon models to combine these data with unobserved properties such as the size distribution of grains, their structure and…

Astrophysics · Physics 2007-05-23 Adolf N. Witt

We study the Quantum Measurement Process in a Stern-Gerlach setup with the spin of a silver atom as the quantum system and the position as the apparatus. The system and the apparatus are treated quantum-mechanically using unitary evolution.…

Quantum Physics · Physics 2016-02-10 Anirudh Reddy , Joseph Samuel , Kumar Shivam , Supurna Sinha

Soft nanocomposites represent both a theoretical and an experimental challenge due to the high number of the microscopic constituents that strongly influence the behaviour of the systems. An effective theoretical description of such systems…

Soft Condensed Matter · Physics 2016-12-14 Emanuele Locatelli , Barbara Capone , Christos N. Likos

The properties of the normal distribution under linear transformation, as well the easy way to compute the covariance matrix of marginals and conditionals, offer a unique opportunity to get an insight about several aspects of uncertainties…

Data Analysis, Statistics and Probability · Physics 2018-02-12 Giulio D'Agostini
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