Veronika Ročková
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Veronika Ročková
Veronika Ročková (born 1985) is a Bayesian statistics, Bayesian statistician. Born in Czechoslovakia, and educated in the Czech Republic, Belgium, and the Netherlands, she works in the US as a professor of econometrics and statistics and James S. Kemper Faculty Scholar at the University of Chicago. Her research studies methods including variable selection, High-dimensional statistics, high-dimensional inference, Mathematical optimization, non-convex optimization, likelihood-free inference, and the spike-and-slab LASSO, and also includes applications in biomedical statistics. Education and career Ročková was born in 1985 in Pardubice, Czechoslovakia, now in the Czech Republic. She studied mathematics and statistics at Charles University in Prague, Hasselt University in Belgium, and Erasmus University Rotterdam in the Netherlands. She earned a bachelor's degree in mathematics from Charles University in 2007, a master's degree in biostatistics from Hasselt University in 2009, a s ...
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Bayesian Statistics
Bayesian statistics ( or ) is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a ''degree of belief'' in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation, which views probability as the limit of the relative frequency of an event after many trials. More concretely, analysis in Bayesian methods codifies prior knowledge in the form of a prior distribution. Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Bayes' theorem describes the conditional probability of an event based on data as well as prior information or beliefs about the event or conditions related to the event. For example, in Bayesian inference, Bayes' theorem can ...
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