Substitute Models for Structural Components Loads Estimation Based on Flight Parameters and Statistical Inference Methods

2019 ◽  
pp. 1128-1137
Author(s):  
Michal Dziendzikowski ◽  
Wojciech Zielinski ◽  
Piotr Reymer ◽  
Marcin Kurdelski ◽  
Piotr Synaszko ◽  
...  
Keyword(s):  
Statistical Inference ◽  
Structural Components ◽  
Flight Parameters ◽  
Inference Methods

scholarly journals Interpretable factor models of single-cell RNA-seq via variational autoencoders

2019 ◽  
Author(s):  
Valentine Svensson ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Statistical Inference ◽  
Factor Models ◽  
Rna Seq ◽  
Cell Type ◽  
Massive Datasets ◽  
Domain Specific ◽  
Variational Autoencoder ◽  
Inference Methods

Single cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable. We present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with the auto-encoding variational Bayes framework, is not domain specific and may be of interest for other applications.

scholarly journals Buchbesprechungen

2016 ◽  
Vol 25 (2) ◽  
Author(s):  
Josef Roppert ◽  
Jürgen Pilz ◽  
Sylvia Frühwirth-Schnatter ◽  
Walter Katzenbeisser ◽  
Reinhard Viertl ◽  
...  
Keyword(s):  
Brownian Motion ◽  
Wavelet Analysis ◽  
Statistical Inference ◽  
Nonlinear Regression ◽  
Observational Studies ◽  
Statistical Modelling ◽  
Statistical Tools ◽  
Likelihood Functions ◽  
Inference Methods

Probability (A.N. Shiryaev)Advanced Statistics. Volume I: Description of Populations (S.J. Haberman)Tools for Statistical Inference. Methods for the Exploration of Posterior Distributi-ons and Likelihood Functions (M.A Tanner)Plane Answers to Complex Questions. The Theory of LinearModels (R. Christensen)Statistical Tools for Nonlinear Regression (S. Huet, A. Bouvier, M. Gruet und E. Jolivet)Handbook of Brownian Motion: Facts and Formulae (A.N. Borodin und P. Salminen)Bayes’sche Statistik für kontrollierte Experimente (K. Felsenstein)Applied Wavelet Analysis with S-Plus (A. Bruce und H. Gao)Observational Studies (P.R. Rosenbaum)Activity-Based Statistics (R.L. Scheaffer, M. Gnanadesikan, A.Watkins und J.Witmer)Statistical Modelling (G.U.H. Seeber, B.J. Francis, R. Hatzinger und G. Steckel-Berger)

Mathematical networks for thermal transient and non-transient progressive fatigue of engine components

1998 ◽  
Vol 212 (2) ◽  
pp. 125-136
Author(s):  
H Azzam
Keyword(s):  
Fatigue Damage ◽  
Damage Mechanism ◽  
Structural Components ◽  
Micro Structure ◽  
Computational Costs ◽  
Fatigue Management ◽  
Flight Parameters ◽  
Thermal Transient ◽  
Engine Components ◽  
Life Consumption

Engine components can experience varying centrifugal loads, gas loads, oxidation, micro- structure transformation at high temperatures and stresses induced by temperature gradients. The life consumption of hot engine components depends not only on these factors but also on the time spent at constant-amplitude loads. The damage mechanism of engine components is therefore complex and requires formidable models. These models are not suitable for fatigue management or on-board systems because of their high computational costs. There is a need for efficient simulations that can accurately portray this complex damage mechanism and, at the same time, can be embedded in fatigue management and on-board systems. Mathematical networks were developed to fulfil this need and successfully synthesized the fatigue damage of aircraft structural components from flight parameters. In this paper, the feasibility of training the mathematical networks to synthesize fatigue of engine components is demonstrated. The mathematical attributes of the networks were based on information supplied by Rolls-Royce. The networks’ training mechanism was targeted at the minimization of errors in synthesized accumulative damage values. The mathematical networks synthesized the accumulative fatigue damage of three engine components successfully. One component was subject to non-thermal transient stresses and two components were subject to thermal transient stresses.

Estimating Small-Area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods

2006 ◽  
Vol 10 (4) ◽  
pp. 577-598 ◽  
Author(s):  
Qiang Cai ◽  
Gerard Rushton ◽  
Budhendra Bhaduri ◽  
Edward Bright ◽  
Phillip Coleman
Keyword(s):  
Statistical Inference ◽  
Small Area ◽  
Spatial Interpolation ◽  
Inference Methods ◽  
Age And Sex

scholarly journals Statistical Inference Methods for Two Crossing Survival Curves: A Comparison of Methods

PLoS ONE ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. e0116774 ◽  
Author(s):  
Huimin Li ◽  
Dong Han ◽  
Yawen Hou ◽  
Huilin Chen ◽  
Zheng Chen
Keyword(s):  
Statistical Inference ◽  
Comparison Of Methods ◽  
Survival Curves ◽  
Inference Methods
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