scholarly journals Imprecise credibility theory

2021 ◽  
pp. 1-15
Author(s):  
Liang Hong ◽  
Ryan Martin
Keyword(s):  
Real World ◽  
Prior Information ◽  
Model Specification ◽  
Credibility Theory ◽  
Experience Rating ◽  
Credibility Estimator ◽  
Real World Applications ◽  
Property And Casualty Insurance ◽  
Doubly Robust ◽  
Interval Estimator

Abstract The classical credibility theory is a cornerstone of experience rating, especially in the field of property and casualty insurance. An obstacle to putting the credibility theory into practice is the conversion of available prior information into a precise choice of crucial hyperparameters. In most real-world applications, the information necessary to justify a precise choice is lacking, so we propose an imprecise credibility estimator that honestly acknowledges the imprecision in the hyperparameter specification. This results in an interval estimator that is doubly robust in the sense that it retains the credibility estimator’s freedom from model specification and fast asymptotic concentration, while simultaneously being insensitive to prior hyperparameter specification.

scholarly journals Generative Facial Prior for Large-Factor Blind Face Super-Resolution

2021 ◽  
Vol 2078 (1) ◽  
pp. 012045
Author(s):  
Xiaomeng Guo ◽  
Li Yi ◽  
Hang Zou ◽  
Yining Gao
Keyword(s):  
Real World ◽  
Prior Information ◽  
Super Resolution ◽  
Feature Transformation ◽  
Magnification Factor ◽  
High Quality ◽  
Large Factor ◽  
Real World Applications ◽  
The Face ◽  
The Rich

Abstract Most existing face super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic down sampling). However, these methods suffer a severe performance drop in various unknown degradations in real-world applications. Previous methods usually rely on facial priors, such as facial geometry prior or reference prior, to restore realistic face details. Nevertheless, low-quality inputs cannot provide accurate geometric priors while high-quality references are often unavailable, which limits the use of face super-resolution in real-world scenes. In this work, we propose GPLSR which used the rich priors encapsulated in the pre-trained face GAN network to perform blind face super-resolution. This generative facial priori is introduced into the face super-resolution process through channel squeeze-and-excitation spatial feature transformation layer (SE-SFT), which makes our method achieve a good balance between realness and fidelity. Moreover, GPLSR can restores facial details with single forward pass because of powerful generative facial prior information. Extensive experiment shows that when the magnification factor is 16, this method achieves better performance than existing techniques in both synthetic and real datasets.

scholarly journals The Linear Markov Property in Credibility Theory

1987 ◽  
Vol 17 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Thomas Witting
Keyword(s):  
Sufficient Conditions ◽  
Markov Property ◽  
Recent Time ◽  
Necessary And Sufficient Conditions ◽  
Credibility Theory ◽  
Experience Rating ◽  
Credibility Estimator ◽  
Time Period ◽  
Loss Reserving ◽  
Necessary And Sufficient

AbstractWe study the linear Markov property, i.e. the possibility of basing the credibility estimator on data of the most recent time period without loss of accuracy. Necessary and sufficient conditions are derived generally. The meaning of the linear Markov property is also discussed in different experience rating and loss reserving models.

Semi-supervised community detection using label propagation

2014 ◽  
Vol 28 (29) ◽  
pp. 1450208 ◽  
Author(s):  
Dong Liu ◽  
Hong-Yu Bai ◽  
Hui-Jia Li ◽  
Wen-Jun Wang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Prior Information ◽  
Label Propagation ◽  
Background Information ◽  
Network Nodes ◽  
Detection Approach ◽  
Real World Applications ◽  
Topology Information ◽  
Almost All

Almost all existing approaches for community detection only make use of the network topology information, which completely ignore the background information of the network. However, in many real world applications, we may know some prior information that could be useful in detecting the community structures. Specifically, the true community assignments of certain nodes are known in advance. In this paper, a novel semi-supervised community detection approach is proposed based on label propagation, which can utilize prior information to guide the discovery process of community structure. Our algorithm can propagate the labels from the labeled nodes to the whole network nodes. The algorithm is evaluated on several artificial and real-world networks and shows that it is highly effective in recovering communities.

scholarly journals Electronic Phenomena of Transition Metal Oxides

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 256
Author(s):  
Christian Rodenbücher ◽  
Kristof Szot
Keyword(s):  
Solid State ◽  
Transition Metal ◽  
Metal Oxides ◽  
Real World ◽  
Transition Metal Oxides ◽  
Major Research ◽  
Research Fields ◽  
Real World Applications

Transition metal oxides with ABO3 or BO2 structures have become one of the major research fields in solid state science, as they exhibit an impressive variety of unusual and exotic phenomena with potential for their exploitation in real-world applications [...]

scholarly journals Applications of Distributed-Order Fractional Operators: A Review

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 110
Author(s):  
Wei Ding ◽  
Sansit Patnaik ◽  
Sai Sidhardh ◽  
Fabio Semperlotti
Keyword(s):  
Fractional Calculus ◽  
Real World ◽  
Transport Processes ◽  
Model Systems ◽  
Variable Order ◽  
Fractional Operators ◽  
Research Activity ◽  
Road Map ◽  
Real World Applications ◽  
Distributed Order

Distributed-order fractional calculus (DOFC) is a rapidly emerging branch of the broader area of fractional calculus that has important and far-reaching applications for the modeling of complex systems. DOFC generalizes the intrinsic multiscale nature of constant and variable-order fractional operators opening significant opportunities to model systems whose behavior stems from the complex interplay and superposition of nonlocal and memory effects occurring over a multitude of scales. In recent years, a significant amount of studies focusing on mathematical aspects and real-world applications of DOFC have been produced. However, a systematic review of the available literature and of the state-of-the-art of DOFC as it pertains, specifically, to real-world applications is still lacking. This review article is intended to provide the reader a road map to understand the early development of DOFC and the progressive evolution and application to the modeling of complex real-world problems. The review starts by offering a brief introduction to the mathematics of DOFC, including analytical and numerical methods, and it continues providing an extensive overview of the applications of DOFC to fields like viscoelasticity, transport processes, and control theory that have seen most of the research activity to date.

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