Bayesian Techniques in Spatial and Network Econometrics: 2. Computational Methods and Algorithms

1995 ◽  
Vol 27 (4) ◽  
pp. 615-644 ◽  
Author(s):  
L W Hepple
Keyword(s):  
Bayesian Approach ◽  
Spatial Econometrics ◽  
Broad Class ◽  
Bayesian Computation ◽  
Model Estimation ◽  
Great Cost ◽  
Considerable Potential ◽  
Bayesian Techniques ◽  
Recent Developments ◽  
The Bayesian Approach

Bayesian theory has been seen as having considerable potential and attractiveness for model estimation and analysis in spatial and network econometrics. However, analytical and computational problems have also been seen as a great barrier. In this paper the analytical simplifications available are developed and the algorithms required are examined. The author argues that, for a broad class of models in spatial econometrics, Bayesian analysis is quite practicable and can be implemented without great cost. The spatial specifications are mapped into the various forms of Bayesian computation available and detailed examples are provided. Recent developments on the frontier of Bayesian computation have potential to expand further the practical applicability of the Bayesian approach to spatial econometrics.

scholarly journals The promise of Bayesian analysis for prominence seismology

2013 ◽  
Vol 8 (S300) ◽  
pp. 393-394 ◽  
Author(s):  
Iñigo Arregui ◽  
Andrés Asensio Ramos ◽  
Antonio J. Díaz
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Model Comparison ◽  
Physical Models ◽  
Physical Parameters ◽  
Solar Prominence ◽  
Bayesian Techniques ◽  
The Bayesian Approach

AbstractWe propose and use Bayesian techniques for the determination of physical parameters in solar prominence plasmas, combining observational and theoretical properties of waves and oscillations. The Bayesian approach also enables to perform model comparison to assess how plausible alternative physical models/mechanisms are in view of data.

scholarly journals The Bayesian Approach and the Results of the ISAR-REACT 5 Trial

2021 ◽  
Vol 14 (2) ◽  
pp. 231-232
Author(s):  
Adnan Kastrati ◽  
Alexander Hapfelmeier
Keyword(s):  
Bayesian Approach ◽  
The Bayesian Approach

A new Bayesian approach for QTL mapping of family data

2021 ◽  
Author(s):  
Daiane Aparecida Zuanetti ◽  
Luis Aparecido Milan
Keyword(s):  
Qtl Mapping ◽  
Random Effects ◽  
Bayesian Approach ◽  
Variance Component ◽  
Mendelian Inheritance ◽  
Family Data ◽  
Data Sets ◽  
Mendelian Segregation ◽  
Gaw17 Data ◽  
The Bayesian Approach

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs’ effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents’ genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs’ position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.

scholarly journals Generalized functionals of Brownian motion

1994 ◽  
Vol 7 (3) ◽  
pp. 247-267
Author(s):  
N. U. Ahmed
Keyword(s):  
Brownian Motion ◽  
Sobolev Spaces ◽  
Measure Space ◽  
Broad Class ◽  
Wiener Measure ◽  
Multiple Stochastic Integrals ◽  
Finite Dimensional ◽  
Nonlinear Functionals ◽  
Recent Developments ◽  
Locally Convex

In this paper we discuss some recent developments in the theory of generalized functionals of Brownian motion. First we give a brief summary of the Wiener-Ito multiple Integrals. We discuss some of their basic properties, and related functional analysis on Wiener measure space. then we discuss the generalized functionals constructed by Hida. The generalized functionals of Hida are based on L2-Sobolev spaces, thereby, admitting only Hs, s∈R valued kernels in the multiple stochastic integrals. These functionals are much more general than the classical Wiener-Ito class. The more recent development, due to the author, introduces a much more broad class of generalized functionals which are based on Lp-Sobolev spaces admitting kernels from the spaces 𝒲p,s, s∈R. This allows analysis of a very broad class of nonlinear functionals of Brownian motion, which can not be handled by either the Wiener-Ito class or the Hida class. For s≤0, they represent generalized functionals on the Wiener measure space like Schwarz distributions on finite dimensional spaces. In this paper we also introduce some further generalizations, and construct a locally convex topological vector space of generalized functionals. We also present some discussion on the applications of these results.

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