The Contribution of Young Researchers to Bayesian Statistics

Name: The Contribution of Young Researchers to Bayesian Statistics | Proceedings of BAYSM2013
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Proceedings of BAYSM2013

Ettore Lanzarone Francesca Ieva(Editor)

Springer (Publisher)

1st Edition

Published on 22. November 2013

X, 214 pages

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978-3-319-02084-6 (ISBN)

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Ettore Lanzarone is a Permanent Researcher at the division of Milan of the Institute of Applied Mathematics and Information Technology (IMATI) of the National Research Council of Italy (CNR), Milan, Italy. He is also Adjunct Professor Mathematical Analysis at the Politecnico di Milano, Milan, Italy. He obtained his Ph.D. in Bioengineering in June 2008 at the Politecnico di Milano, and his master degree in Biomedical Engineering cum laude in April 2004 at the Politecnico di Milano. He is member of the European Working Group on Operational Research Applied to Health Services (ORAHS) and of the Italian National Bioengineering Group (GNB). His current research interests include: parameter estimation and stochastic evolution of dynamic systems described by ordinary and partial differential equations; stochastic models for estimating the demand and planning the activities in healthcare structures; modelling and in-vitro studies of the cardiovascular fluid dynamics.

Francesca Ieva is a Postdoctoral Research Fellow at the Modeling and Scientific Computing Lab (MOX), Department of Mathematics, Politecnico di Milano, Milan, Italy. She obtained her Ph.D. in Mathematical Models and Methods for Engineering and her master degree in Mathematical Engineering at the Politecnico di Milano in 2012 and 2008, respectively. She is member of ISBA (and Program Chair of the junior section), RSS, SIS (and Chair of the young section) and SIAM. Her research activities include Statistical Learning in Biomedical context, focused on modelling data arising from integration of clinical surveys and administrative databanks, Clinical Biostatistics, Healthcare assessment, Mixed Effects Models and Semi-parametric Bayesian hierarchical models, Depth Measures and Multivariate Functional Data Analysis for applications to ECG signals, Multi State Models for the analysis of chronic diseases progression like heart failures.

Content

Intro
Preface
Contents
Part I Theoretical Bayes
1 A Nonparametric Model for Stationary Time Series
1.1 Introduction
1.2 The Model
1.2.1 Illustrations
References
2 Estimation of Optimally Combined-Biomarker Accuracy in the Absence of a Gold Standard Reference Test
2.1 Introduction
2.2 Methods
2.3 Results
2.4 Conclusions
References
3 On Bayesian Transformation Selection:Problem Formulation and Preliminary Results
3.1 Introduction
3.2 Bayesian Formulation
3.3 Results
3.4 Conclusions
References
4 A Simple Proof for the Multinomial Version of the Representation Theorem
4.1 Introduction
4.2 De Finetti's Method for Multinomial Trials
References
5 A Sequential Monte Carlo Framework for Adaptive Bayesian Model Discrimination Designs Using MutualInformation
5.1 Introduction
5.2 Notation
5.3 Sequential Monte Carlo Incorporating Model Uncertainty
5.4 Mutual Information for Model Discrimination
5.5 Examples
5.6 Conclusion
References
6 Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System
6.1 Introduction
6.2 Method
6.2.1 State-Space Model
6.2.2 Rao-Blackwellized Particle Filter
6.3 Experimental Results
6.3.1 Dataset Simulation
6.3.2 Validation of the RBPF Algorithm
6.4 Conclusions
References
7 Adaptive Bayes Test for Monotonicity
7.1 Introduction
7.2 Theoretical Results
7.3 Conclusion
References
8 Bayesian Inference on Individual-Based Models by Controlling the Random Inputs
8.1 Introduction
8.2 Controlling Random Inputs
8.3 Woodhoopoe Model
8.4 Summary of the Talk
References
9 Consistency of Bayesian Nonparametric Hidden Markov Models
9.1 Introduction
9.2 The Model
9.3 Consistency
References
10 Bayesian Methodology in the Stochastic Event Reconstruction Problems
10.1 Introduction
10.2 Theoretical Preliminaries
10.3 Methods and Results
References
Part II Computational Bayes
11 Efficient Fitting of Bayesian Regression Models with Spatio-Temporally Varying Coefficients
11.1 Introduction
11.2 A Spatio-Temporal Model
11.2.1 Parameterisation, Marginalisation and Interweaving
11.2.2 Model Specifications
11.3 Results
11.4 Summary
References
12 PAWL-Forced Simulated Tempering
12.1 A Parallel Adaptive Wang-Landau Algorithm
12.2 Simulated Tempering
12.3 Conclusion
References
13 Approximate Bayesian Computation for the Elimination of Nuisance Parameters
13.1 Introduction
13.2 The Elimination of Nuisance Parameters
13.2.1 Examples
13.3 Conclusions
References
14 Reweighting Schemes Based on Particle Methods
14.1 Introduction
14.2 Particle Move-Reweighting Strategies
14.3 Closing Remarks
References
15 A Bayesian Nonparametric Framework to Inference on Totals of Finite Populations
15.1 Introduction
15.2 Inference on Planned Domains
15.2.1 Posterior Point Estimates
15.2.2 Full Posterior Inference
15.3 Simulation Results
15.4 Discussion
References
16 Parallel Slice Sampling
16.1 Introduction
16.2 The Algorithm
16.2.1 First Step
16.2.2 Second Step
16.3 A Simple Example
17 Approximate Bayesian Computation in Quantile Regression
17.1 Summary
References
Part III Bayes @ Work: Appraisal of Applications to the Real World
18 Spatiotemporal Model for Short-Term Predictions of Air Pollution Data
18.1 Introduction
18.2 Data-Fusion Model
18.3 Analyses and Results
References
19 Predicting Rainfall Fields from Lightning Records: A Hierarchical Bayesian Approach
19.1 Introduction
19.2 The Model
19.2.1 The Fixed Effect
19.2.2 The Spatial Component
19.3 Estimation of Parameters and Preliminary Results
References
20 Bayesian Approach to Environmental Problem Based on PFLOTRAN Package
20.1 Introduction
20.2 Problem
20.3 Bayesian Approach
20.4 Results and Discussion
References
21 Bayesian Hierarchical Modeling of Growth via Gompertz Model: An Application in Poultry
21.1 Introduction
21.2 Material and Method
21.3 Results
References
22 Bayesian Prediction of SMART Power Semiconductor Lifetime with Bayesian Networks
22.1 Introduction
22.2 Data Characteristics and Available Information
22.3 Model Development and Evaluation Results
22.4 Summary
References
23 Consumer-Oriented New-Product Development in Fruit Flavor Breeding: A Bayesian Approach
23.1 Introduction
23.2 Material and Methods
23.3 Results
23.4 Concluding Remarks
References
24 Bayesian Layer Counting in Ice-Cores: Reconstructing the Time Scale
24.1 Introduction
24.2 The Model
24.2.1 Priors
24.3 MCMC Implementation
24.3.1 Updating ?: Maintaining the Cycle Count
24.3.2 Updating ?: Changing the Cycle Count
24.3.3 Hyper-parameters
24.4 Conclusions
References
Part IV A Bayesian Approach to Biostatistics and Health Sciences
25 Bayesian Analysis and Prediction of Patients' Demands for Visits in Home Care
25.1 Introduction
25.2 Bayesian Model
25.3 Application to a Real Case
25.4 Results
25.5 Conclusion
References
26 Exploiting Adaptive Bayesian Regression Shrinkageto Identify Exome Sequence Variants Associatedwith Gene Expression
26.1 Introduction
26.2 Modelling Using the Normal-Gamma Prior
26.2.1 Including Uncertainty in Gene Expression (y)
26.2.2 Including Uncertainty in SNP Calls (X)
26.2.3 Including Functional Annotation Information
26.3 Preliminary Results
26.4 Conclusion
References
27 Randomized Phase II Trials: A Bayesian Two-Stage Design
27.1 Introduction
27.2 A Bayesian Two-Stage Design
27.3 Numerical Results
References
28 Bayesian Matrix Factorization for Outlier Detection: An Application in Population Genetics
28.1 Introduction
28.2 Bayesian Matrix Factorization for Outlier Detection
28.2.1 Model
28.2.2 Posterior Inference and Algorithm
28.3 Results
28.4 Conclusions
References
29 Noise Model Selection for Multichannel Diffusion-Weighted MRI
29.1 Introduction
29.2 Background
29.2.1 Data
29.2.2 Models
29.2.3 Methods
29.3 Result Summary
References
30 Analysis of Hospitalizations of Patients Affected by Chronic Heart Disease
30.1 Introduction
30.2 The Dataset
30.3 The Model
30.4 Posterior Inference
30.5 Conclusion
References
31 A Semiparametric Bayesian Multivariate Model for Survival Probabilities After Acute Myocardial Infarction
31.1 Introduction
31.2 The Bayesian Model in a Nutshell
References
32 Particle Learning Approach to Bayesian Model Selection: An Application from Neurology
32.1 Introduction
32.2 The Neuromuscular Model
32.3 Methodology
32.4 Discussion
References
Part V Bayesian Models for Stochastic and Economic Processes
33 Analysis of Italian Financial Market via Bayesian Dynamic Covariance Models
33.1 Bayesian Covariance Regression for Financial Data
33.2 Application to National Stock Indices (NSI)
References
34 Bayesian Model Selection of Regular Vine Copulas
34.1 Introduction
34.2 Regular Vine Copulas
34.3 Model Selection Algorithm
General Reversible Jump MCMC-Based Model Selection
34.4 Simulation Study and Application Study
Reference
35 Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models
35.1 Introduction
35.2 Factor SV Estimation
35.3 Application
References
36 On Some Stationary Models: Construction and Estimation
36.1 Markovian Models with Given Marginal Distributions
36.2 Poisson Weighted Density
36.2.1 Estimation
References
37 Claim Sizes in the Compound Poisson Processfrom a Bayesian Viewpoint
37.1 Introduction
37.2 Estimation of the Claim Count and Claim Amount Distribution
References
38 Land Rental Market and Agricultural Production Efficiency: A Bayesian Perspective
38.1 Introduction
38.2 Research Methodology
38.3 Data and Estimation Strategy
38.4 Empirical Results
38.5 Conclusions
References
Part VI Suggestions for Young Researchers
39 The Point Is.to Publish?
39.1 From the Big Technical Rules to My WHW Rules
39.2 The Point Is to Publish
39.3 How to Share and the RELUKE Rule
39.4 Presenting a Paper in a Talk: My WHW Rules
39.5 Writing a Manuscript for Submission: My WHW Rules
39.6 .and the Grand Final: Submission and the Publishing Process
References

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The Contribution of Young Researchers to Bayesian Statistics

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