What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
William D. Penny mainly investigates Artificial intelligence, Bayes' theorem, Bayesian probability, Bayesian inference and Machine learning.
His Artificial intelligence study integrates concerns from other disciplines, such as Causal model and Pattern recognition.
His Causal model research is multidisciplinary, relying on both Cognition, Mathematical model and Dynamic causal modelling.
William D. Penny regularly ties together related areas like Linear model in his Bayes' theorem studies.
His studies examine the connections between Bayesian inference and genetics, as well as such issues in Hyperparameter, with regards to Restricted maximum likelihood, Laplace's method, Mathematical optimization, Covariance and Gibbs sampling.
His biological study spans a wide range of topics, including Information theory, Cognitive psychology, Functional magnetic resonance imaging and Functional integration.
His most cited work include:
- Dynamic causal modelling. (3336 citations)
- Statistical Parametric Mapping: The Analysis of Functional Brain Images (2078 citations)
- Bayesian model selection for group studies. (1053 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Bayesian inference, Machine learning, Bayesian probability and Pattern recognition.
His study in Algorithm extends to Artificial intelligence with its themes.
His Machine learning research also works with subjects such as
- Hidden Markov model that connect with fields like Bounded rationality,
- Dynamic causal modelling which connect with Functional neuroimaging.
He interconnects Generalized linear model, Data mining and Linear model in the investigation of issues within Bayesian probability.
His research in Pattern recognition focuses on subjects like Electroencephalography, which are connected to Neuroimaging and Elementary cognitive task.
The concepts of his Bayes' theorem study are interwoven with issues in Overfitting and Causal model.
He most often published in these fields:
- Artificial intelligence (56.19%)
- Bayesian inference (29.38%)
- Machine learning (26.29%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (56.19%)
- Neuroscience (18.56%)
- Bayesian inference (29.38%)
In recent papers he was focusing on the following fields of study:
William D. Penny mainly focuses on Artificial intelligence, Neuroscience, Bayesian inference, Inference and Set.
The Artificial intelligence study combines topics in areas such as Machine learning, Empirical research and Pattern recognition.
His work in the fields of Artificial neural network and Transfer of learning overlaps with other areas such as Field.
His study on Neuroimaging, Electroencephalography, Motor cortex and Prefrontal cortex is often connected to Variable as part of broader study in Neuroscience.
His work is dedicated to discovering how Bayesian inference, Bayes' theorem are connected with Algorithm, Dissociation and Impulsivity and other disciplines.
William D. Penny has researched Inference in several fields, including Stimulus and Multivariate analysis.
Between 2014 and 2021, his most popular works were:
- Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation. (62 citations)
- The Neural Representation of Prospective Choice during Spatial Planning and Decisions. (49 citations)
- Causal evidence that intrinsic beta-frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS. (49 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Neuroscience, Bayes' theorem, Motor cortex, Brain mapping and Markov chain Monte Carlo are his primary areas of study.
William D. Penny combines subjects such as Pattern recognition and Bayesian inference with his study of Bayes' theorem.
His studies in Pattern recognition integrate themes in fields like Bayes estimator and Machine learning.
His research integrates issues of Beta Rhythm, Motor system, Dynamic causal modelling and Pyramidal tracts in his study of Motor cortex.
His Brain mapping research integrates issues from Frontal lobe, Functional magnetic resonance imaging and Prefrontal cortex.
Many of his research projects under Artificial intelligence are closely connected to Detector and Group-level effects with Detector and Group-level effects, tying the diverse disciplines of science together.
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