A Bayesian Nonlinear Regression Model Based on t-Distributed Errors
IX Hotine-Marussi Symposium on Mathematical GeodesyInternational Association of Geodesy Symposia(2019)
Abstract
In this contribution, a robust Bayesian approach to adjusting a nonlinear regression model with t-distributed errors is presented. In this approach the calculation of the posterior model parameters is feasible without linearisation of the functional model. Furthermore, the integration of prior model parameters in the form of any family of prior distributions is demonstrated. Since the posterior density is then generally non-conjugated, Monte Carlo methods are used to solve for the posterior numerically. The desired parameters are approximated by means of Markov chain Monte Carlo using Gibbs samplers and Metropolis-Hastings algorithms. The result of the presented approach is analysed by means of a closed-loop simulation and a real world application involving GNSS observations with synthetic outliers.
MoreTranslated text
Key words
Bayesian nonlinear regression model, Gibbs sampler, Markov Chain Monte Carlo, Metropolis-Hastings algorithm, Scaled t-distribution
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined