Approaches to robust process identification: A review and tutorial of probabilistic methods
Industrial data sets are often contaminated with outliers due to sensor malfunctions, signal
interference, and other disturbances as well as interplay of various other factors. The effect …
interference, and other disturbances as well as interplay of various other factors. The effect …
On the convergence of black-box variational inference
We provide the first convergence guarantee for black-box variational inference (BBVI) with
the reparameterization gradient. While preliminary investigations worked on simplified …
the reparameterization gradient. While preliminary investigations worked on simplified …
A robust Gaussian approximate fixed-interval smoother for nonlinear systems with heavy-tailed process and measurement noises
In this letter, a robust Gaussian approximate (GA) fixed-interval smoother for nonlinear
systems with heavy-tailed process and measurement noises is proposed. The process and …
systems with heavy-tailed process and measurement noises is proposed. The process and …
A variational Bayesian approach to robust sensor fusion based on Student-t distribution
H Zhu, H Leung, Z He - Information Sciences, 2013 - Elsevier
In this paper, a robust sensor fusion method is proposed where the measurement noise is
modeled by a Student-t distribution. The Student-t distribution has a heavy tail compared to …
modeled by a Student-t distribution. The Student-t distribution has a heavy tail compared to …
Bayesian infinite mixture models for wind speed distribution estimation
Wind energy, as a clean, environment-friendly, and inexhaustible renewable energy, has
attracted significant attention, and wind speed distribution plays an important role in its …
attracted significant attention, and wind speed distribution plays an important role in its …
A robust particle filtering algorithm with non-Gaussian measurement noise using student-t distribution
D Xu, C Shen, F Shen - IEEE Signal Processing Letters, 2013 - ieeexplore.ieee.org
The Gaussian noise assumption may result in a major decline in state estimation accuracy
when the measurements are with the presence of outliers. In this letter, we endow the …
when the measurements are with the presence of outliers. In this letter, we endow the …
Sparse heteroscedastic multiple spline regression models for wind turbine power curve modeling
An accurate wind turbine power curve (WTPC) plays a vital role in wind power forecasting
and wind turbine condition monitoring. There are two major shortcomings of current WTPC …
and wind turbine condition monitoring. There are two major shortcomings of current WTPC …
Robust consensus nonlinear information filter for distributed sensor networks with measurement outliers
The traditional consensus-based filters are widely used in distributed sensor networks.
However, they suffer from divergence when outliers occur. This paper proposes a robust …
However, they suffer from divergence when outliers occur. This paper proposes a robust …
Parameter estimation of heavy-tailed AR model with missing data via stochastic EM
The autoregressive (AR) model is a widely used model to understand time series data.
Traditionally, the innovation noise of the AR is modeled as Gaussian. However, many time …
Traditionally, the innovation noise of the AR is modeled as Gaussian. However, many time …
Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks
This paper presents an artificial neural network-based model of domestic water
consumption. The model is based on real-world data collected from smart meters, and …
consumption. The model is based on real-world data collected from smart meters, and …