What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Machine learning
His primary areas of investigation include Bayesian probability, Statistical model, Econometrics, Bayesian inference and Mathematical optimization.
The Bayesian probability study combines topics in areas such as Poisson distribution, Meteorology, Kernel and Population abundance.
Statistical model is the subject of his research, which falls under Statistics.
His work deals with themes such as Variable-order Bayesian network, Bayes' theorem, Data science and Nonlinear system, which intersect with Econometrics.
His work in the fields of Bayesian statistics overlaps with other areas such as Process.
His biological study deals with issues like Kriging, which deal with fields such as Kalman filter, Dimensionality reduction and Autoregressive model.
His most cited work include:
- Statistics for Spatio-Temporal Data (1245 citations)
- Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling (404 citations)
- A dimension-reduced approach to space-time Kalman filtering (336 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Bayesian probability, Econometrics, Statistical model, Statistics and Data mining.
His Bayesian probability study focuses on Bayesian inference in particular.
His Bayesian inference research is multidisciplinary, incorporating perspectives in Algorithm and Gibbs sampling.
His study in Econometrics is interdisciplinary in nature, drawing from both Markov chain Monte Carlo, American Community Survey, Bayesian hierarchical modeling, Multivariate statistics and Mixed model.
His Statistical model study integrates concerns from other disciplines, such as Inference and Curse of dimensionality.
As part of his studies on Statistics, Christopher K. Wikle frequently links adjacent subjects like Hierarchical database model.
He most often published in these fields:
- Bayesian probability (34.80%)
- Econometrics (24.51%)
- Statistical model (18.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Bayesian probability (34.80%)
- Artificial intelligence (7.35%)
- Inference (5.88%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Bayesian probability, Artificial intelligence, Inference, Machine learning and Algorithm.
His work carried out in the field of Bayesian probability brings together such families of science as American Community Survey and Covariate.
A large part of his Artificial intelligence studies is devoted to Statistical model.
His Inference research integrates issues from Smoothing, Sampling, Statistics, Latent variable and Markov model.
In general Machine learning, his work in Deep learning is often linked to Process linking many areas of study.
His research integrates issues of Probabilistic forecasting and Nonlinear system in his study of Algorithm.
Between 2018 and 2021, his most popular works were:
- Spatio-Temporal Statistics with R (46 citations)
- Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data (30 citations)
- Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting (28 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Ecology
Christopher K. Wikle focuses on Gibbs sampling, Nonlinear system, Bayesian probability, Recurrent neural network and Data mining.
Christopher K. Wikle has researched Gibbs sampling in several fields, including Generalized linear mixed model, Logit, Conditional probability distribution, Econometrics and Bayesian hierarchical modeling.
His Nonlinear system study incorporates themes from Smoothing, Algorithm and Data assimilation.
His Algorithm research is multidisciplinary, relying on both Probabilistic forecasting and Conditional dependence.
The various areas that Christopher K. Wikle examines in his Bayesian probability study include Temporal database and Machine learning, Deep learning.
His studies in Data mining integrate themes in fields like Reservoir computing and Spatial analysis.
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