What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Machine learning
Bani K. Mallick spends much of his time researching Bayesian probability, Markov chain Monte Carlo, Statistics, Artificial intelligence and Bayes' theorem.
Bani K. Mallick interconnects Distribution, Inference, Multivariate adaptive regression splines, Applied mathematics and Algorithm in the investigation of issues within Bayesian probability.
His Markov chain Monte Carlo research is multidisciplinary, incorporating perspectives in Prediction interval, Hierarchical database model and Bayesian inference.
His research in Statistics tackles topics such as Econometrics which are related to areas like Independent and identically distributed random variables, Parametric family and Parametric statistics.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His work deals with themes such as Risk analysis, Visualization and Prior probability, which intersect with Bayes' theorem.
His most cited work include:
- Automatic Bayesian curve fitting (338 citations)
- Gene selection: a Bayesian variable selection approach (337 citations)
- A Bayesian CART algorithm (219 citations)
What are the main themes of his work throughout his whole career to date?
Bani K. Mallick mostly deals with Bayesian probability, Artificial intelligence, Markov chain Monte Carlo, Statistics and Algorithm.
His study in Bayesian probability is interdisciplinary in nature, drawing from both Data mining and Econometrics.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
His research on Markov chain Monte Carlo also deals with topics like
- Mathematical optimization that connect with fields like Uncertainty quantification,
- Posterior probability that connect with fields like Markov chain.
In Statistics, Bani K. Mallick works on issues like Applied mathematics, which are connected to Reversible-jump Markov chain Monte Carlo.
His studies in Algorithm integrate themes in fields like Covariance, Multivariate statistics and Model selection.
He most often published in these fields:
- Bayesian probability (44.34%)
- Artificial intelligence (24.06%)
- Markov chain Monte Carlo (23.11%)
What were the highlights of his more recent work (between 2017-2021)?
- Bayesian probability (44.34%)
- Algorithm (19.34%)
- Prior probability (15.09%)
In recent papers he was focusing on the following fields of study:
Bani K. Mallick focuses on Bayesian probability, Algorithm, Prior probability, Artificial intelligence and Graphical model.
His Bayesian probability research entails a greater understanding of Statistics.
His studies deal with areas such as Ensemble forecasting, Multivariate statistics and Trend analysis as well as Algorithm.
The various areas that Bani K. Mallick examines in his Prior probability study include Shrinkage, Computation and Inference.
His Artificial intelligence research includes themes of Machine learning and Pattern recognition.
He interconnects Sampling, Spline and Multivariate adaptive regression splines in the investigation of issues within Markov chain Monte Carlo.
Between 2017 and 2021, his most popular works were:
- Estimation of COVID-19 spread curves integrating global data and borrowing information (52 citations)
- Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm (27 citations)
- Two-Stage Metropolis-Hastings for Tall Data (12 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Machine learning
His primary scientific interests are in Bayesian probability, Algorithm, Prior probability, Linear regression and Bayes' theorem.
In his articles, Bani K. Mallick combines various disciplines, including Bayesian probability and Cholesky decomposition.
His research integrates issues of Ensemble forecasting, Trend analysis and Time series in his study of Algorithm.
His Prior probability research is included under the broader classification of Statistics.
His research investigates the link between Linear regression and topics such as Feature selection that cross with problems in Dimensionality reduction, Covariate and Minimax.
His Data integration research is multidisciplinary, relying on both Regression, Accelerated failure time model, Survival analysis, Artificial intelligence and Machine learning.
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