What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Her primary areas of study are Bayesian inference, Bayesian probability, Cross-validation, Artificial intelligence and Algorithm.
Her studies deal with areas such as Inference and Data mining as well as Bayesian probability.
Her Data mining research integrates issues from Bayesian data analysis and Markov chain.
Her research investigates the connection with Markov chain and areas like Probabilistic programming language which intersect with concerns in Dynamic Bayesian network.
Aki Vehtari has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
Her Algorithm study integrates concerns from other disciplines, such as Prior probability and Markov chain Monte Carlo.
Her most cited work include:
- Bayesian Data Analysis (13605 citations)
- Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (1166 citations)
- Understanding predictive information criteria for Bayesian models (915 citations)
What are the main themes of her work throughout her whole career to date?
Bayesian probability, Artificial intelligence, Algorithm, Gaussian process and Bayesian inference are her primary areas of study.
Her Bayesian probability study combines topics in areas such as Cross-validation, Data mining and Model selection.
Projection is closely connected to Feature selection in her research, which is encompassed under the umbrella topic of Data mining.
Aki Vehtari focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Bayesian statistics.
Aki Vehtari focuses mostly in the field of Algorithm, narrowing it down to matters related to Markov chain Monte Carlo and, in some cases, Applied mathematics, Prior probability, Laplace's method and Hyperparameter.
Her Gaussian process research is multidisciplinary, incorporating elements of Covariance, Covariance function, Approximate inference and Mathematical optimization.
She most often published in these fields:
- Bayesian probability (33.01%)
- Artificial intelligence (24.60%)
- Algorithm (22.65%)
What were the highlights of her more recent work (between 2018-2021)?
- Bayesian probability (33.01%)
- Algorithm (22.65%)
- Artificial intelligence (24.60%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Bayesian probability, Algorithm, Artificial intelligence, Bayesian inference and Inference.
Her work deals with themes such as Cross-validation, Data mining, Importance sampling, Model selection and Applied mathematics, which intersect with Bayesian probability.
Her Algorithm research includes elements of Sampling, Gaussian process and Predictive inference.
Her Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Causal inference.
As a member of one scientific family, Aki Vehtari mostly works in the field of Bayesian inference, focusing on Markov chain Monte Carlo and, on occasion, Hyperparameter and Bayesian statistics.
Her study looks at the relationship between Inference and topics such as Posterior probability, which overlap with Markov chain.
Between 2018 and 2021, her most popular works were:
- Visualization in Bayesian workflow (176 citations)
- R-squared for Bayesian regression models (144 citations)
- Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra. (68 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Bayesian probability, Inference, Bayesian inference, Algorithm and Artificial intelligence are her primary areas of study.
The concepts of her Bayesian probability study are interwoven with issues in Model checking, Cross-validation, Data mining and Gaussian process.
Aki Vehtari combines subjects such as Longitudinal study and Random effects model with her study of Data mining.
She performs integrative study on Bayesian inference and Context in her works.
Her work in Algorithm addresses issues such as Markov chain Monte Carlo, which are connected to fields such as Normalization, Heavy-tailed distribution, Quantile, Bayesian statistics and Importance sampling.
She has included themes like Machine learning and Pattern recognition in her Artificial intelligence study.
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