MaxEnt 2019 - 34 articles
Garching, Germany | 30 June–5 July 2019
Volume Editors:
Udo von Toussaint, Max-Planck-Institut for Plasmaphysics, Germany
Roland Preuss, Max-Planck-Institut for Plasmaphysics, Germany
The spin echo experiment is an important tool in magnetic resonance for exploring the coupling of spin systems to their local environment. The strong couplings in a typical Electron Spin Resonance (ESR) experiment lead to rapid relaxation effects tha...
Many inference problems relate to a dynamical system, as represented by dx/dt = f (x), where x ∈ ℝn is the state vector and f is the (in general nonlinear) system function or model. Since the time of Newton, researchers have pondered the problem of s...
The key input quantity to climate modelling and weather forecasts is the solar beam irradiance, i.e., the primary amount of energy provided by the sun. Despite its importance the absolute accuracy of the measurements are limited—which not only...
In this paper we focus on the estimation of mutual information from finite samples
Many environmental and genetic conditions may modify jaws growth. In orthodontics, the right treatment timing is crucial. This timing is a function of the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM is important. In orthodont...
Formants are characteristic frequency components in human speech that are caused by resonances in the vocal tract during speech production. They are of primary concern in acoustic phonetics and speech recognition. Despite this, making accurate measur...
In these days of fast-paced business, accurate automatic color or pattern detection is a necessity for carpet retailers. Many well-known color detection algorithms have many shortcomings. Apart from the color itself, neighboring colors, style, and pa...
Within a Bayesian framework we propose a non-intrusive reduced-order spectral approach (polynomial chaos expansion) to assess the uncertainty of ion-solid interaction simulations. The method not only reduces the number of function evaluations but pro...
A number of Unidentified Aerial Phenomena (UAP) encountered by military, commercial, and civilian aircraft have been reported to be structured craft that exhibit `impossible’ flight characteristics. We consider the 2004 UAP encounters with the...
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...
Classification and clustering problems are closely connected with pattern recognition where many general algorithms have been developed and used in various fields. Depending on the complexity of patterns in data, classification and clustering procedu...
Aortic dissection is a cardiovascular disease with a disconcertingly high mortality. When it comes to diagnosis, medical imaging techniques such as Computed Tomography, Magnetic Resonance Tomography or Ultrasound certainly do the job, but also have t...
Fluorescently targeted proteins are widely used for studies of intracellular organelles dynamic. Peripheral proteins are transiently associated with organelles and a significant fraction of them are located at the cytosol. Image analysis of periphera...
Galilean Monte Carlo (GMC) allows exploration in a big space along systematic trajectories, thus evading the square-root inefficiency of independent steps. Galilean Monte Carlo has greater generality and power than its historical precursor Hamiltonia...
Information Geometry conflicts with the independence that is required for science and for rational inference generally.
A stable and unique solution to the ill-posed inverse problem in radio synthesis image analysis is sought employing Bayesian probability theory combined with a probabilistic two-component mixture model. The solution of the ill-posed inverse problem i...
Existing algorithms like nested sampling and annealed importance sampling are able to produce accurate estimates of the marginal likelihood of a model, but tend to scale poorly to large data sets. This is because these algorithms need to recalculate...
Randomization is an integral part of well-designed statistical trials, and is also a required procedure in legal systems. Implementation of honest, unbiased, understandable, secure, traceable, auditable and collusion resistant randomization procedure...
We present here Nested_fit, a Bayesian data analysis code developed for investigations of atomic spectra and other physical data. It is based on the nested sampling algorithm with the implementation of an upgraded lawn mower robot method for finding...
The method of maximum entropy is used to model curved physical space in terms of points defined with a finite resolution. Such a blurred space is automatically endowed with a metric given by information geometry. The corresponding space-time is such...
Signale and image processing has always been the main tools in many area and in particular in Medical and Biomedical applications. Nowadays, there are great number of toolboxes, general purpose and very specialized, in which classical techniques are...
In empirical science, random sampling is the golden standard to ensure unbiased, impartial, or fair results, as it works as a technological barrier designed to prevent spurious communication or illegitimate interference between parties in the applica...
In the Entropic Dynamics (ED) framework quantum theory is derived as an application of entropic methods of inference. The physics is introduced through appropriate choices of variables and of constraints that codify the relevant physical information....
We study the dynamics of information processing in the continuous depth limit of deep feed-forward Neural Networks (NN) and find that it can be described in language similar to the Renormalization Group (RG). The association of concepts to patterns b...
Uncertainty Quantification (UQ) is highly requested in computational modeling and simulation, especially in an industrial context. With the continuous evolution of modern complex systems demands on quality and reliability of simulation models increas...
As key building blocks for modern data processing and analysis methods—ranging from AI, ML and UQ to model comparison, density estimation and parameter estimation—Bayesian inference and entropic concepts are in the center of this rapidly growing rese...
We present a novel implementation of the adaptively annealed thermodynamic integration technique using Hamiltonian Monte Carlo (HMC). Thermodynamic integration with importance sampling and adaptive annealing is an especially useful method for estimat...
What is the probability that ball lightning (BL) is a real phenomenon of nature? The answer depends on your prior information. If you are one of those lucky men who had a close encounter with a BL and escaped unscathed, your probability that it is re...
3D X-ray Computed Tomography (CT) is used in medicine and non-destructive testing (NDT) for industry to visualize the interior of a volume and control its healthiness. Compared to analytical reconstruction methods, model-based iterative reconstructio...
A method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential equation with localized sources. The approach is appli...
Conditional value at risk (CVaR), or expected shortfall, is a risk measure for investments according to Rockafellar and Uryasev. Yamai and Yoshiba define CVaR as the conditional expectation of loss given that the loss is beyond the value at risk (VaR...
An analysis tool using Adaptive Kernel to solve an ill-posed inverse problem for a 2D model space is introduced. It is applicable for linear and non-linear forward models, for example in tomography and image reconstruction. While an optimisation base...
In this work, we propose an approach to better understand the effects of neuronal noise on neural communication systems. Here, we extend the fundamental Hodgkin-Huxley (HH) model by adding synaptic couplings to represent the statistical dependencies...
The Bayesian approach Maximum a Posteriori (MAP) is discussed in the context of solving the image reconstruction problem in nuclear medicine: positron emission tomography (PET) and single photon emission computer tomography (SPECT). Two standard prob...
