[PDF] Top 20 Bayesian structural inference with applications in social science
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Bayesian structural inference with applications in social science
... To try to investigate this more fully, and to understand the influence of other ex- planatory factors, we employed a range of analyses. First, we carried out a logistic regression that predicts whether an individual ... See full document
211
Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs
... practicing Bayesian nonparametrics is to relax some unreal- istic assumptions on data, such as homogeneity and ...nonparametric Bayesian prior 1 encoding some special structures, which indirectly 2 ... See full document
49
Bayesian inference on non stationary data
... presence of a structural break is envisaged. In this case the above testing procedures tend to over-estimate the order of integration In Perron (1989) the conclusions of Nelson and Plosser (1982) are endorsed for ... See full document
215
Learning of model discrepancy for structural dynamics applications using Bayesian history matching
... Bayesian History Matching (BHM) is a ‘likelihood-free’ method for calibrating computer models (here defined as simulators) under the assumption of model discrepancy, i.e. given the simulator was evaluated with the ... See full document
15
Bayesian Learning for Earthquake Engineering Applications and Structural Health Monitoring
... SVM is a machine learning algorithm that has been used as an efficient tool in bioinformatics, computer science, and ,to a much lesser extent, civil engineering. In classification, SVM determines the separating ... See full document
144
Bayesian inference for indirectly observed stochastic processes, applications to epidemic modelling
... Epidemic models are often used to simulate disease transmission dynamics, detect emerg- ing outbreaks (Unkel et al., 2012), and assess public health interventions (Boily et al., 2007). In order to capture the dynamics of ... See full document
154
Bayesian Inference of Stochastic Volatility Models and Applications in Risk Management.
... We may note that SRISK is the amount of capital shortfall of an individual firm during a crisis. It is not a quantitative measure of all the costs to society by a financial firm’s bankruptcy. In Acharya et al. (2011), ... See full document
111
A generalisation of bayesian inference : with applications to finite population sampling theory
... Hacking, in The Emergence Of Probability, suggests the latter view. Probability, as a mathematical science, emerged suddenly in the mid-seventeenth century. Hacking conjectures that its sudden prominence was due ... See full document
162
Bayesian inference for nonlinear structural time series models
... in applications where the derivatives are considerably simpler than the transition equations—the performance of the ADPF is around 5 times better than the standard particle ... See full document
30
Essays on Bayesian Inference with Applications to Open Economy Macroeconomics.
... The empirical analysis of the two deficits is substantially important as a matter of rebalanc- ing the external deficit. However, the empirical literature investigating the relationship between the two deficits has ... See full document
114
Bayesian inference with monotone instrumental variables
... Under the monotone instrumental variable (MIV) assumption introduced by Manski and Pepper (2000), mean responses vary monotonically across speci- fied sub-populations defined by the MIV. It has a wide application ... See full document
27
Bayesian inference for protein signalling networks
... dimensions, Bayesian variable selection requires multiplicity correction in order to avoid degeneracy [Scott and Berger, ...(ii) Inference was based upon a local score borrowed from Bayesian linear ... See full document
128
Crafting a Lightweight Bayesian Inference Engine
... This inference engine consists of the aforementioned four class types is implemented in Python programming language to be a reusable module as shown in Appendix ...probabilistic inference ... See full document
6
Bayesian Inference For The Segmented Weibull Distribution
... In this case, the literature presents many papers with classical or Bayesian ap- proaches to get inferences for a change-point assuming the exponential distribution which is a special case of the Weibull ... See full document
19
Exact Bayesian inference for the Bingham distribution
... a Bayesian frame- work. Walker (2013) considered Bayesian inference for the Bingham distribution which removes the need to compute the normalising constant, using a (more gen- eral) method that was ... See full document
12
Bayesian Inference of State Space Models with Flexible Covariance Matrix Rank: Applications for Inflation Modeling
... using Bayesian, or more precisely, Markov Chain Monte Carlo (MCMC) techniques to conduct estimation; (2) orthogo- nal innovations; and (3) introducing stochastic volatility a la, ... See full document
146
Bayesian Inference for Finite State Transducers
... For both EM and Bayesian methods, different train- ing runs yield different results. EM’s objective func- tion (probability of observed data) is very bumpy for the unsupervised problems we work on—different ... See full document
9
Bayesian Inference Under Shape Constraints.
... for Bayesian estimation of a shape- constrained function, it seems worth exploring if a point estimator resulting from the induced posterior can be used in quantifying uncertainty in estimating the value of the ... See full document
142
Bayesian Inference in Nonparanormal Graphical Models.
... the Bayesian method based on GCGM (Mohammadi et ...the Bayesian method (Mulgrave & Ghosal, 2017) in a nonparanormal graphical model, and the empirical method (Liu et ...the Bayesian method of ... See full document
107
Collapsed Variational Bayesian Inference for PCFGs
... We have also sketched an alternative CVB al- gorithm which makes a harsher independence as- sumption for the latent variables but then requires no approximation of the variational posterior by performing inference ... See full document
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