What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
Herbert Hoijtink mostly deals with Econometrics, Bayes factor, Statistics, Prior probability and Gibbs sampling.
His work on p-value as part of general Econometrics study is frequently connected to Data interpretation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Linear model, Bayesian linear regression and Model selection as well as Bayes factor.
His work carried out in the field of Prior probability brings together such families of science as Element, Analysis of covariance, Posterior probability and Bayesian inference.
His studies examine the connections between Gibbs sampling and genetics, as well as such issues in Applied mathematics, with regards to Goodness of fit, Latent class model and Posterior predictive distribution.
Herbert Hoijtink interconnects Normal approximation and Analysis of variance in the investigation of issues within Bayesian probability.
His most cited work include:
- Redefine statistical significance (993 citations)
- Bayesian estimation and testing of structural equation models (210 citations)
- Inequality Constrained Analysis of Variance: A Bayesian Approach. (162 citations)
What are the main themes of his work throughout his whole career to date?
Herbert Hoijtink spends much of his time researching Econometrics, Statistics, Bayesian probability, Bayes factor and Prior probability.
Herbert Hoijtink has included themes like Statistical hypothesis testing, Bayesian statistics, Bayesian inference, Item response theory and Statistical model in his Econometrics study.
His study in the fields of Rasch model, Latent class model, Multivariate normal distribution and Multivariate statistics under the domain of Statistics overlaps with other disciplines such as Expectation–maximization algorithm.
His Bayesian probability study also includes
- Data mining that connect with fields like Cluster analysis,
- Null hypothesis, which have a strong connection to Developmental psychology.
His Bayes factor study integrates concerns from other disciplines, such as Machine learning, Model selection and Frequentist probability.
His study looks at the relationship between Prior probability and fields such as Posterior probability, as well as how they intersect with chemical problems.
He most often published in these fields:
- Econometrics (34.53%)
- Statistics (30.22%)
- Bayesian probability (28.06%)
What were the highlights of his more recent work (between 2014-2021)?
- Bayes factor (28.06%)
- Statistics (30.22%)
- Bayesian probability (28.06%)
In recent papers he was focusing on the following fields of study:
Herbert Hoijtink mainly investigates Bayes factor, Statistics, Bayesian probability, Econometrics and Bayes' theorem.
His Bayes factor research incorporates elements of Statistical hypothesis testing, Machine learning, Statistical model and Frequentist probability.
His work on Alternative hypothesis, Sample size determination and Variance decomposition of forecast errors as part of general Statistics study is frequently linked to R package and Law of total variance, therefore connecting diverse disciplines of science.
His study in Bayesian probability is interdisciplinary in nature, drawing from both Probability and statistics, Structural equation modeling, Multivariate statistics and Data mining.
He studies Econometrics, focusing on p-value in particular.
His work investigates the relationship between Bayes' theorem and topics such as Bayesian inference that intersect with problems in Probabilistic logic, Electroencephalography and Frequency.
Between 2014 and 2021, his most popular works were:
- Redefine statistical significance (993 citations)
- A tutorial on testing hypotheses using the Bayes factor. (30 citations)
- P300 amplitude variations, prior probabilities, and likelihoods: A Bayesian ERP study. (28 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His scientific interests lie mostly in Econometrics, Bayes factor, Statistics, Bayesian probability and Bayes' theorem.
In Econometrics, Herbert Hoijtink works on issues like Statistical significance, which are connected to Replication crisis.
Bayes factor is a subfield of Artificial intelligence that Herbert Hoijtink studies.
The various areas that Herbert Hoijtink examines in his Bayesian probability study include Reliability, Cognitive psychology, Cognition and Convergent validity.
His Bayes' theorem study incorporates themes from Statistical hypothesis testing, Frequentist probability and Bayesian inference.
His Prior probability research is multidisciplinary, incorporating perspectives in Null hypothesis and Type I and type II errors.
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