Bayesian Markov Chain Monte Carlo analysis
Evolutionary History of Rabies in Ghana
... Phylogenetic analysis of the sequences obtained confirmed all viruses to be RABV, belonging to lineages previously detected in sub-Saharan ...Phylogeographic Bayesian Markov chain Monte ...
11
A fully Bayesian approach to shape estimation of objects from tomography data using MFS forward solutions
... a Bayesian perspective of inverse ...of analysis involves domain discretization and the use of the finite element ...Then, Bayesian statistical modelling will be dis- cussed with specific examples ...
22
Bayesian Model Selection for Genome-Wide Epistatic Quantitative Trait Loci Analysis
... jump Markov chain Monte Carlo (MCMC) We consider experimental crosses derived from two algorithm, introduced by Green (1995), offers a power- inbred ...only Bayesian model selection ...
12
Stochastic gradient Markov chain Monte Carlo
... a Bayesian approach to data analysis, but the continual growth in the size of the data sets in these fields prevents the use of traditional MCMC ...scalable Monte Carlo algorithms. Broadly ...
31
Parallel Markov Chain Monte Carlo
... of Bayesian inference permits prior knowledge to temper and guide the processing of the image data, and with reversible-jump MCMC allows for the uncertain dimensionality (the number of dimensions a model has may ...
209
Component Oriented Reliability Analysis Based on Hierarchical Bayesian Model for an Open Source Software
... In this paper, we focus on an OSS developed under open source project. We discuss the method of component- oriented software reliability assessment considering the fault-detection rate of each component based on ...
8
Exploring the Impact of Work Life Balance on the Employee and Organisational Growth
... to Bayesian Inference”, John Wiley and Sons, ...applications”, Bayesian Statistics ( ...of Markov Chain Monte Carlo Methods in A Bayesian Analysis of the Block and ...
7
Markov Chain Monte Carlo Technology
... on Markov chains whose stationary distribution is the probability distribution of ...as Markov chain Monte Carlo methods, or simply MCMC meth- ods, have been influential in the modern ...
35
Stochastic volatility with leverage: fast likelihood inference
... Kim, Shephard, and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method ...
23
Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors
... Component Analysis (PCA)-based ...probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation ...
23
Bayesian Methods for Nonlinear and Discrete Data with Complex Dependence.
... Gelfand & Smith, 1990). In the sections to follow, we will focus on the more complicated process of obtaining posterior samples for the adjustable parameters, ϑ . We will detail several Markov chain ...
100
Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia
... Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for ...of Bayesian joint modeling of HIV/AIDS data obtained from Bale Robe General ...
9
Bayesian Forecasting of Stock Prices Via the Ohlson Model
... The Bayesian approaches in Chapter 2 and Chapter 3 represents two extreme ...hierarchical Bayesian approach is to simultaneously estimate the unknown coefficients for each company by adaptively pooling ...
80
Bayesian Analysis
... of Bayesian methods is the ability to formally include this type of information in a statistical analysis through the use of informative ...doing Bayesian inference; indeed, much applied work uses ...
13
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
... (see, for example, Fichtner et al., 2006a, b). Smooth univer- sal approximators, such as artificial neural networks, are one possibility; Gaussian process latent variable models (Titsias and Lawrence, 2010) and Gaussian ...
20
Multi-Objective ROC learning for classification
... In this thesis a multi-objective evolutionary algorithm (MOEA) is used to find clas- sifiers whose ROC graph locations are Pareto optimal. The Relevance Vector Machine (RVM) is a state-of-the-art classifier that produces ...
5
Bayesian InferenceA pproach to Inverse P roblems in aFi nancial MathematicalM odel
... a Bayesian inference ...by Markov Chain Monte Carlo (MCMC), which explores the poste- rior state ...the Bayesian inference ...the Bayesian inference approach can ...
14
Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics
... the analysis of array-based comparative genomic hybridization (array-CGH) data, where the covariates are measurements of DNA quantity at m locations in the genome, collected for n ...
29
Psychology in econometric models: conceptual and methodological foundations
... econometric analysis is the fact that the econome- trician can only observe a part of the factors relevant for an economic problem of interest - the problem of endogenous ...
32
II. DEVELOPING A NEW ALGORITHM
... (Markov Chain Monte Carlo Multiple Imputation), MCMC SI (Markov Chain Monte Carlo Single Imputation) and MS (Mean Substitution) over different percentages of ...
6