PubAg

Main content area

You searched for: Subject "Bayesian theory" Remove constraint Subject: "Bayesian theory"
Start Over

Search

5,305  Search Results

« Previous | 1 - 20 of 5,305 |
Number of results to display per page

Search Results

1. Bayesian hidden Markov models for dependent large-scale multiple testing

Author:
Wang, Xia; Shojaie, Ali; Zou, Jian
Source:
Computational statistics & data analysis 2019 v.136 pp. 123-136
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; Markov chain; algorithms;     Show all 3 Subjects
Abstract:
... An optimal and flexible multiple hypotheses testing procedure is constructed for dependent data based on Bayesian techniques, aiming at handling two challenges, namely dependence structure and non-null distribution specification. Ignoring dependence among hypotheses tests may lead to loss of efficiency and bias in decision. Misspecification in the non-null distribution, on the other hand, can resu ...
DOI:
10.1016/j.csda.2019.01.009

2. Application of Bayesian Inference in Fourier-Transformed Alternating Current Voltammetry for Electrode Kinetic Mechanism Distinction

Author:
Li, Jiezhen; Kennedy, Gareth F.; Gundry, Luke; Bond, Alan M.; Zhang, Jie
Source:
Analytical chemistry 2019 v.91 no.8 pp. 5303-5309
ISSN:
1520-6882
Subject:
Bayesian theory,   etc   ; electrochemistry; electrodes; models;     Show all 4 Subjects
Abstract:
... Estimation of parameters of interest in dynamic electrochemical (voltammetric) studies is usually undertaken via heuristic or data optimization comparison of the experimental results with theory based on a model chosen to mimic the experiment. Typically, only single point parameter values are obtained via either of these strategies without error estimates. In this article, Bayesian inference is in ...
DOI:
10.1021/acs.analchem.9b00129

3. A variable selection package driving Netica with Python

Author:
Beuzen, Tomas; Simmons, Joshua
Source:
Environmental modelling & software 2019 v.115 pp. 1-5
ISSN:
1364-8152
Subject:
Bayesian theory,   etc   ; artificial intelligence; computer software; models;     Show all 4 Subjects
Abstract:
... Bayesian Networks (BNs) are useful methods of probabilistically modelling environmental systems. BN performance is sensitive to the number of variables included in the model framework. The selection of the optimum set of variables to include in a BN (“variable selection”) is therefore a key part of the BN modelling process. While variable selection is an issue dealt with in the wider BN and machin ...
DOI:
10.1016/j.envsoft.2019.01.018

4. Skeletal age-at-death estimation: Bayesian versus regression methods

Author:
Nikita, Efthymia; Nikitas, Panos
Source:
Forensic science international 2019 v.297 pp. 56-64
ISSN:
0379-0738
Subject:
Bayesian theory,   etc   ; linear models; prediction; regression analysis;     Show all 4 Subjects
Abstract:
... Age-at-death estimation in a skeletal assemblage (target sample) is biased by the demographic profile of the material used for age prediction (training sample) when this profile is different from that of the target sample. This bias is minimized if the demographic profile of the target sample is properly taken into account in the method developed for age-at-death estimation. In the Bayesian approa ...
DOI:
10.1016/j.forsciint.2019.01.033

5. Ensemble bayesian forecasting system part ii: experiments and properties

Author:
Herr, Henry D.; Krzysztofowicz, Roman
Source:
Journal of hydrology 2019
ISSN:
0022-1694
Subject:
Bayesian theory,   etc   ; algorithms; hydrologic models; rivers; uncertainty;     Show all 5 Subjects
Abstract:
... A typical deficiency of ensemble forecasts is the lack of calibration; e.g., ensemble members are under-dispersed and, consequently, probabilities estimated from them cannot be taken at their face values. Self-calibration is one of the unique theoretic properties of the Bayesian forecasting system, BFS. Its ensemble version, EBFS, inherits this property, provided the ensemble size is large enough. ...
DOI:
10.1016/j.jhydrol.2019.02.057

6. Moment conditions and Bayesian non‐parametrics

Author:
Bornn, Luke; Shephard, Neil; Solgi, Reza
Source:
Journal of the Royal Statistical Society 2019 v.81 no.1 pp. 5-43
ISSN:
1369-7412
Subject:
Bayesian theory,   etc   ; equations; methodology; models; probability;     Show all 5 Subjects
Abstract:
... Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non‐parametric Bayesian set‐up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on re ...
DOI:
10.1111/rssb.12294

7. On the Problem of Modeling a Fundamental Niche from Occurrence Data

Author:
Jiménez, Laura; Soberón, Jorge; Christen, J. Andrés; Soto, Desireé
Source:
Ecological modelling 2019
ISSN:
0304-3800
Subject:
Bayesian theory,   etc   ; computer software; models; niches;     Show all 4 Subjects
Abstract:
... We address the problem of how to estimate a hypothetical fundamental niche from presence data. We do this by: a) postulating a simple shape for the physiologically defined niche (Hutchinson’s fundamental niche), b) providing prior information for the estimation of the parameters that define the shape of the niche, and c) interpreting the results taking into account how the actual observables are c ...
DOI:
10.1016/j.ecolmodel.2019.01.020

8. The Hydrologist’s Guide to Bayesian Model Selection, Averaging and Combination

Author:
Höge, M.; Guthke, A.; Nowak, W.
Source:
Journal of hydrology 2019
ISSN:
0022-1694
Subject:
Bayesian theory,   etc   ; flavor; hydrology; models; uncertainty;     Show all 5 Subjects
Abstract:
... Model selection and model averaging have become popular tools to address conceptual uncertainty in hydro(geo)logical modeling. Within the last two decades, many different flavors of approaches and implementations have emerged which complicate an easy access to and a thorough understanding of the underlying principles. With the many approaches and applications, a variety of terms has been defined, ...
DOI:
10.1016/j.jhydrol.2019.01.072

9. Sequential Bayesian inference for static parameters in dynamic state space models

Author:
Bhattacharya, Arnab; Wilson, Simon P.
Source:
Computational statistics & data analysis 2018 v.127 pp. 187-203
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; models; prediction;     Show all 3 Subjects
Abstract:
... A method for sequential Bayesian inference of the static parameters of a dynamic state space model is proposed. The method is able to use any valid approximation to the filtering and prediction densities of the state process. It computes the posterior distribution of the static parameters on a discrete grid that tracks the support dynamically. For inference of the state process, the Kalman filter ...
DOI:
10.1016/j.csda.2018.05.018

10. Covariate selection with group lasso and doubly robust estimation of causal effects

Author:
Koch, Brandon; Vock, David M.; Wolfson, Julian
Source:
Biometrics 2018 v.74 no.1 pp. 8-17
ISSN:
0006-341X
Subject:
Bayesian theory,   etc   ; biometry; models;     Show all 3 Subjects
Abstract:
... The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given stud ...
DOI:
10.1111/biom.12736
PubMed:
28636276
PubMed Central:
PMC5740021

11. Melissa: Bayesian clustering and imputation of single-cell methylomes

Author:
Kapourani, Chantriolnt-Andreas; Sanguinetti, Guido
Source:
Genome biology 2019 v.20 no.1 pp. 61
ISSN:
1474-760X
Subject:
Bayesian theory,   etc   ; data collection; epigenetics; methylation; quantitative analysis;     Show all 5 Subjects
Abstract:
... Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epige ...
DOI:
10.1186/s13059-019-1665-8

12. A Bayesian approach to mapping the uncertainties of global urban lands

Author:
Ouyang, Zutao; Fan, Peilei; Chen, Jiquan; Lafortezza, Raffaele; Messina, Joseph P.; Giannico, Vincenzo; John, Ranjeet
Source:
Landscape and urban planning 2019 v.187 pp. 210-218
ISSN:
0169-2046
Subject:
Bayesian theory,   etc   ; geographical distribution; uncertainty; urban areas; urban planning;     Show all 5 Subjects
Abstract:
... Global distribution of urban lands is one of the essential pieces of information necessary for urban planning. However, large disagreement exists among different products and the uncertainty remains difficult to quantify. We applied a Bayesian approach to map the uncertainties of global urban lands. We demonstrated the approach by producing a hybrid global urban land map that synthesized five diff ...
DOI:
10.1016/j.landurbplan.2018.07.016

13. A decision-making model for flood warning system based on ensemble forecasts

Author:
Goodarzi, Leila; Banihabib, Mohammad Ebrahim; Roozbahani, Abbas
Source:
Journal of hydrology 2019 v.573 pp. 207-219
ISSN:
0022-1694
Subject:
Bayesian theory,   etc   ; basins; hydrology; models; storms; uncertainty; watersheds; Iran;     Show all 8 Subjects
Abstract:
... The purpose of this study is to develop a flood warning system based on Atmospheric Ensemble Forecasts. Although ensemble forecasts are increasingly employed for flood forecasting, developing a flood warning system based on ensemble forecasts has not been adequately addressed yet. In this study, first a Weather Research and Forecasting (WRF) model was used to forecast the heavy precipitation in Ka ...
DOI:
10.1016/j.jhydrol.2019.03.040

14. A novel variational Bayesian method for variable selection in logistic regression models

Author:
Zhang, Chun-Xia; Xu, Shuang; Zhang, Jiang-She
Source:
Computational statistics & data analysis 2019 v.133 pp. 1-19
ISSN:
0167-9473
Subject:
Bayesian theory,   etc   ; Markov chain; models; prediction; regression analysis;     Show all 5 Subjects
Abstract:
... With high-dimensional data emerging in various domains, sparse logistic regression models have gained much interest of researchers. Variable selection plays a key role in both improving the prediction accuracy and enhancing the interpretability of built models. Bayesian variable selection approaches enjoy many advantages such as high selection accuracy, easily incorporating many kinds of prior kno ...
DOI:
10.1016/j.csda.2018.08.025

15. Bayesian compressed vector autoregressions

Author:
Koop, Gary; Korobilis, Dimitris; Pettenuzzo, Davide
Source:
Journal of econometrics 2019 v.210 no.1 pp. 135-154
ISSN:
0304-4076
Subject:
Bayesian theory,   etc   ; econometric models; economic analysis; economic theory; macroeconomics;     Show all 5 Subjects
Abstract:
... Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior ...
DOI:
10.1016/j.jeconom.2018.11.009

16. Bayesian estimation of sparse dynamic factor models with order-independent and ex-post mode identification

Author:
Kaufmann, Sylvia; Schumacher, Christian
Source:
Journal of econometrics 2019 v.210 no.1 pp. 116-134
ISSN:
0304-4076
Subject:
Bayesian theory,   etc   ; econometric models; economic analysis; economic theory; macroeconomics; prices;     Show all 6 Subjects
Abstract:
... Common variation in N series is extracted into k≪N dynamic factors. We induce sparsity by using a zero point mass–normal mixture prior distribution on the loadings. Estimation and rotational identification are independent of variable ordering. Sparsity helps identifying the factor space and the factors. Rotational identification, including factor order and sign, is obtained by processing the poste ...
DOI:
10.1016/j.jeconom.2018.11.008

17. Approximate Bayesian computation with the Wasserstein distance

Author:
Bernton, Espen; Jacob, Pierre E.; Gerber, Mathieu; Robert, Christian P.
Source:
Journal of the Royal Statistical Society 2019 v.81 no.2 pp. 235-269
ISSN:
1369-7412
Subject:
Bayesian theory,   etc   ; data collection; equations; statistical models; time series analysis;     Show all 5 Subjects
Abstract:
... A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values. We propose to avoid the use of summaries and the en ...
DOI:
10.1111/rssb.12312

18. A review of statistical methods to analyze extreme precipitation and temperature events in the Mediterranean region

Author:
Lazoglou, Georgia; Anagnostopoulou, Christina; Tolika, Konstantia; Kolyva-Machera, Fotini
Source:
Theoretical and applied climatology 2019 v.136 no.1-2 pp. 99-117
ISSN:
0177-798X
Subject:
Bayesian theory,   etc   ; socioeconomic factors; statistical analysis; temperature; Mediterranean region;     Show all 5 Subjects
Abstract:
... The increasing trend of the intensity and frequency of temperature and precipitation extremes during the past decades has substantial environmental and socioeconomic impacts. Thus, the objective of the present study is the comparison of several statistical methods of the extreme value theory (EVT) in order to identify which is the most appropriate to analyze the behavior of the extreme precipitati ...
DOI:
10.1007/s00704-018-2467-8

19. Bayesian inference for partially identified smooth convex models

Author:
Liao, Yuan; Simoni, Anna
Source:
Journal of econometrics 2019
ISSN:
0304-4076
Subject:
Bayesian theory,   etc   ; algorithms; econometric models; economic analysis; economic theory;     Show all 5 Subjects
Abstract:
... This paper proposes novel Bayesian procedures for partially identified models when the identified set is convex with a smooth boundary, whose support function is locally smooth with respect to the data distribution. Using the posterior of the identified set, we construct Bayesian credible sets for the identified set, the partially identified parameter and their scalar transformations. These constr ...
DOI:
10.1016/j.jeconom.2019.03.001

20. Multilook SAR intensity image segmentation based on Voronoi tessellation and a Gamma mixture model

Author:
Li, Xiaoli; Zhao, Quanhua; Li, Yu
Source:
Remote sensing letters 2019 v.10 no.3 pp. 254-263
ISSN:
2150-7058
Subject:
Bayesian theory,   etc   ; Markov chain; algorithms; models; remote sensing;     Show all 5 Subjects
Abstract:
... This letter presents an SAR intensity image segmentation algorithm by combining Voronoi tessellation and Gamma Mixture Model (GaMM). By Voronoi tessellation, the domain of the image is partitioned into a collection of sub-regions, which acts on region based segmentation to overcome speckle noise. GaMMs defined on the sub-regions can be used to model the complicate distributions of the homogeneous ...
DOI:
10.1080/2150704X.2018.1547446