What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Random variable
His primary areas of study are Algorithm, Subset simulation, Bayesian probability, Statistics and Monte Carlo method.
His studies link Metropolis–Hastings algorithm with Algorithm.
Siu-Kui Au interconnects Slope stability, Probabilistic analysis of algorithms and Engineering management in the investigation of issues within Subset simulation.
The various areas that he examines in his Bayesian probability study include Statistical hypothesis testing, Probabilistic logic, Modal and Structural health monitoring.
His work on Probability density function and Importance sampling as part of general Statistics research is frequently linked to Umbrella sampling, thereby connecting diverse disciplines of science.
His research in Monte Carlo method is mostly focused on Markov chain Monte Carlo.
His most cited work include:
- Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation (1267 citations)
- Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation (498 citations)
- A new adaptive importance sampling scheme for reliability calculations (316 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Modal, Bayesian probability, Operational Modal Analysis and Subset simulation.
His Algorithm research includes themes of Probabilistic logic, Bayesian inference and System identification.
His studies in Modal integrate themes in fields like Identification, Vibration, Structural engineering, Frequency domain and Modal testing.
Siu-Kui Au works mostly in the field of Bayesian probability, limiting it down to topics relating to Structural health monitoring and, in certain cases, Errors-in-variables models, as a part of the same area of interest.
Siu-Kui Au combines subjects such as Stochastic simulation and Mathematical optimization with his study of Subset simulation.
His Markov chain Monte Carlo study which covers Markov chain that intersects with Markov process.
He most often published in these fields:
- Algorithm (32.94%)
- Modal (31.18%)
- Bayesian probability (25.88%)
What were the highlights of his more recent work (between 2015-2021)?
- Operational Modal Analysis (23.53%)
- Modal (31.18%)
- Algorithm (32.94%)
In recent papers he was focusing on the following fields of study:
Siu-Kui Au mainly investigates Operational Modal Analysis, Modal, Algorithm, Bayesian probability and Identification.
His work carried out in the field of Modal brings together such families of science as Vibration, Modal analysis, Covariance matrix and Frequency domain.
The study incorporates disciplines such as Subset simulation, Machine learning, Artificial intelligence and System identification in addition to Algorithm.
His study with Subset simulation involves better knowledge in Markov chain Monte Carlo.
His research integrates issues of Probabilistic logic and Damage detection in his study of Markov chain Monte Carlo.
The Posterior probability research Siu-Kui Au does as part of his general Bayesian probability study is frequently linked to other disciplines of science, such as Test data, therefore creating a link between diverse domains of science.
Between 2015 and 2021, his most popular works were:
- Fundamental two-stage formulation for Bayesian system identification, Part II: Application to ambient vibration data (53 citations)
- Three-dimensional slope reliability and risk assessment using auxiliary random finite element method (50 citations)
- Fundamental two-stage formulation for Bayesian system identification, Part I: General theory (49 citations)
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