Thomas Wiecki, Morgan Pihl andTomás Capretto
9am PT / 12pm ET / 4pm UTC / 6pm Berlin
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In this panel discussion, we discuss IRT (Item Response Theory), GRM (Graded Response Model) and the advantages to using the Bayesian approach at Alva Labs.
Item response theory, also known as the latent response theory, refers to a family of mathematical models that attempt to explain the relationship between latent traits (unobservable characteristic or attribute) and their manifestations (i.e. observed outcomes, responses or performance). Graded response model (or Ordered Categorical Responses Model) is a family of mathematical models for grading responses.
Alva Labs (About Alva – The science behind our platfom | Alva Labs) uses psychometric tests and machine learning for candidate assessments.
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world class team of Bayesian modelers founded PyMC Labs – the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.
Morgan is Head of R&D at the Swedish startup Alva Labs, a recruitment software provider. He has used PyMC and NumPyro for building best-in-class psychometric assessments that have so far been completed by over 300k users. He did his MSc in psychology at Umeå University.
Tomás is a statistician and data scientist at PyMC Labs who loves solving complex problems. He is one of the main developers of Bambi, the Python library that makes Bayesian linear models accessible for all and is built on top of PyMC. He is a part-time doctorate student in Statistics.
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