Search for dissertations about: "Bayesian model averaging"
Showing result 1 - 5 of 8 swedish dissertations containing the words Bayesian model averaging.
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1. Model Selection and Sparse Modeling
Abstract : Parametric signal models are used in a multitude of signal processing applications. This thesis deals with signals for which there are many candidate models, and it is not a priori known which model is the most appropriate one. READ MORE
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2. Bayesian Phylogenetics and the Evolution of Gall Wasps
Abstract : This thesis concerns the phylogenetic relationships and the evolution of the gall-inducing wasps belonging to the family Cynipidae. Several previous studies have used morphological data to reconstruct the evolution of the family. READ MORE
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3. Essays on forecasting and Bayesian model averaging
Abstract : This thesis, which consists of four chapters, focuses on forecasting in a data-rich environment and related computational issues. Chapter 1, “An embarrassment of riches: Forecasting using large panels” explores the idea of combining forecasts from various indicator models by using Bayesian model averaging (BMA) and compares the predictive performance of BMA with predictive performance of factor models. READ MORE
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4. Learning local predictive accuracy for expert evaluation and forecast combination
Abstract : This thesis consists of four papers that study several topics related to expert evaluation and aggregation. Paper I explores the properties of Bayes factors. Bayes factors, which are used for Bayesian hypothesis testing as well as to aggregate models using Bayesian model averaging, are sometimes observed to behave erratically. READ MORE
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5. Bayesian inference in probabilistic graphical models
Abstract : This thesis consists of four papers studying structure learning and Bayesian inference in probabilistic graphical models for both undirected and directed acyclic graphs (DAGs).Paper A presents a novel algorithm, called the Christmas tree algorithm (CTA), that incrementally construct junction trees for decomposable graphs by adding one node at a time to the underlying graph. READ MORE