Development of a time-varying downscaling model considering non-stationarity using a Bayesian approach

2018 ◽  
Vol 38 (7) ◽  
pp. 3157-3176 ◽  
Author(s):  
Subbarao Pichuka ◽  
Rajib Maity
Keyword(s):  
Bayesian Approach ◽  
Time Varying

Robust Estimation of ARX Models With Time Varying Time Delays Using Variational Bayesian Approach

2018 ◽  
Vol 48 (2) ◽  
pp. 532-542 ◽  
Author(s):  
Yujia Zhao ◽  
Alireza Fatehi ◽  
Biao Huang
Keyword(s):  
Robust Estimation ◽  
Bayesian Approach ◽  
Time Delays ◽  
Time Varying ◽  
Variational Bayesian

Bayesian approach for clustered interval-censored data with time-varying covariate effects

2019 ◽  
Vol 12 (3) ◽  
pp. 457-465
Author(s):  
Yue Zhang ◽  
Xia Wang ◽  
Bin Zhang
Keyword(s):  
Censored Data ◽  
Bayesian Approach ◽  
Time Varying ◽  
Interval Censored Data ◽  
Covariate Effects ◽  
Interval Censored

A Bayesian Approach to Modeling Time-Varying Cointegration and Cointegrating Rank

2017 ◽  
Vol 36 (2) ◽  
pp. 267-277 ◽  
Author(s):  
Chew Lian Chua ◽  
Sarantis Tsiaplias
Keyword(s):  
Bayesian Approach ◽  
Time Varying

scholarly journals A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank

2014 ◽  
Author(s):  
Chew Lian Chua ◽  
Sarantis Tsiaplias
Keyword(s):  
Bayesian Approach ◽  
Time Varying

scholarly journals A Bayesian approach to time-varying latent strengths in pairwise comparisons

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251945
Author(s):  
Blaž Krese ◽  
Erik Štrumbelj
Keyword(s):  
Bayesian Approach ◽  
Gaussian Processes ◽  
Rational Interpolation ◽  
Pairwise Comparisons ◽  
Time Varying ◽  
Data Set ◽  
Additional Information ◽  
Time Component ◽  
Barycentric Rational Interpolation ◽  
Sports Data

The famous Bradley-Terry model for pairwise comparisons is widely used for ranking objects and is often applied to sports data. In this paper we extend the Bradley-Terry model by allowing time-varying latent strengths of compared objects. The time component is modelled with barycentric rational interpolation and Gaussian processes. We also allow for the inclusion of additional information in the form of outcome probabilities. Our models are evaluated and compared on toy data set and real sports data from ATP tennis matches and NBA games. We demonstrated that using Gaussian processes is advantageous compared to barycentric rational interpolation as they are more flexible to model discontinuities and are less sensitive to initial parameters settings. However, all investigated models proved to be robust to over-fitting and perform well with situations of volatile and of constant latent strengths. When using barycentric rational interpolation it has turned out that applying Bayesian approach gives better results than by using MLE. Performance of the models is further improved by incorporating the outcome probabilities.

scholarly journals Adaptive Kalman Filter for Linear Systems with Additive and Multiplicative Noises

2021 ◽  
Author(s):  
Xingkai Yu
Keyword(s):  
Kalman Filter ◽  
Linear Systems ◽  
Bayesian Approach ◽  
Multiplicative Noise ◽  
Measurement Noise ◽  
Time Varying ◽  
Projection Formula ◽  
Adaptive Kalman Filter ◽  
Technical Issue ◽  
Multiplicative Noises

This manuscript investigates adaptive Kalman filter problem of of linear systems with multiplicative and additive noises. The main contributions are stated in two aspects. Firstly, compared with the estimation problem of linear systems with additive noises, we propose an algorithm that is applicable to the linear systems with both additive and multiplicative noises. To solve the technical issue raised by the multiplicative noise, a variational Bayesian approach is proposed. Moreover, the proposed approach is capable of estimating the multiplicative and additive measurement noise covariances as a whole, while the existing algorithms often operate in a separate way. Secondly, in contrast with existing literature, where the covariance of the multiplicative noise is assumed to be fixed and known, we focus on the situation that the covariances of both additive and multiplicative noises are time-varying and unknown. Towards this end, a novel adaptive Kalman filter is proposed to jointly estimate the covariances of multiplicative and additive noises based on projection formula and a VB approach.

scholarly journals Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic

2020 ◽  
Author(s):  
Tenglong Li ◽  
Laura F. White
Keyword(s):  
Bayesian Approach ◽  
Missing Values ◽  
Control Measures ◽  
Reproductive Number ◽  
Time Varying ◽  
Line List ◽  
Epidemic Curve ◽  
Back Calculation ◽  
Reporting Delays ◽  
The Bayesian Approach

AbstractSurveillance is the key of controling the COVID-19 pandemic, and it typically suffers from reporting delays and thus can be misleading. Previous methods for adjusting reporting delays are not particularly appropriate for line list data, which usually have lots of missing values that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. We show this Bayesian approach lead to accurate estimates of the epidemic curve and time-varying reproductive numbers and is robust to deviations from model assumptions. We apply the Bayesian approach to a COVID-19 line list data in Massachusetts and find the reproductive number estimates correspond more closely to the control measures than the ones based on the reported curve.

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