What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Neuroscience
Jean Daunizeau spends much of his time researching Artificial intelligence, Bayesian inference, Bayes' theorem, Machine learning and Bayesian probability.
His research in Artificial intelligence intersects with topics in Dynamic programming and Perceptual learning.
His work carried out in the field of Bayesian inference brings together such families of science as Theoretical computer science, Bioinformatics, Mathematical optimization, Conditional probability distribution and Reinforcement learning.
His study in the fields of Bayes factor under the domain of Bayes' theorem overlaps with other disciplines such as Causality.
His biological study spans a wide range of topics, including Magnetoencephalography, Electroencephalography, Functional integration, Neuroimaging and Robustness.
His study in the field of Bayesian statistics is also linked to topics like Random effects model.
His most cited work include:
- Bayesian model selection for group studies. (1053 citations)
- Ten simple rules for dynamic causal modeling. (584 citations)
- Ten simple rules for dynamic causal modeling. (584 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Bayesian inference, Cognitive psychology, Bayesian probability and Machine learning.
The Artificial intelligence study combines topics in areas such as Electroencephalography and Pattern recognition.
Jean Daunizeau interconnects Selection and Statistical model in the investigation of issues within Bayesian inference.
His Bayesian probability study incorporates themes from Algorithm and Causal model.
He has researched Machine learning in several fields, including Neuroimaging, Prior probability, Dynamic causal modelling and Frequentist inference.
His work on Bayes factor as part of general Bayes' theorem study is frequently linked to Causality, therefore connecting diverse disciplines of science.
He most often published in these fields:
- Artificial intelligence (56.69%)
- Bayesian inference (40.13%)
- Cognitive psychology (47.13%)
What were the highlights of his more recent work (between 2017-2021)?
- Cognitive psychology (47.13%)
- Cognition (29.30%)
- Bayesian probability (29.94%)
In recent papers he was focusing on the following fields of study:
Cognitive psychology, Cognition, Bayesian probability, Context and Causal model are his primary areas of study.
His studies deal with areas such as Metacognition, Cognitive dissonance and Flexibility as well as Cognitive psychology.
His work on Social cognition as part of general Cognition study is frequently linked to Certainty and Function, bridging the gap between disciplines.
Jean Daunizeau studies Bayesian inference, a branch of Bayesian probability.
His research in Bayesian inference intersects with topics in Susceptible individual and Hidden Markov model.
Jean Daunizeau interconnects Dynamic causal modelling and Econometrics in the investigation of issues within Causal model.
Between 2017 and 2021, his most popular works were:
- Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases. (46 citations)
- Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases. (46 citations)
- Dynamic causal modelling of COVID-19. (24 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Neuroscience
His primary scientific interests are in Cognitive psychology, Bayesian probability, Econometrics, Causal model and Mortality rate.
His Cognitive psychology research is multidisciplinary, incorporating perspectives in Cognitive dissonance and Functional neuroimaging, Neuroimaging.
His biological study spans a wide range of topics, including Dynamic causal modelling and Susceptible individual.
His Econometrics study integrates concerns from other disciplines, such as Test strategy and Time horizon.
He brings together Mortality rate and Bayesian inference to produce work in his papers.
His work on Bayesian inference is being expanded to include thematically relevant topics such as Hidden Markov model.
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