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aurimas.eu | ||
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doomlab.github.io
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| | | | | Research Overview Interests Psycholinguistics and memory: Semantic memory, associative memory, priming of memory types, normed databases, linguistic variables, memory maps, judgments of memory, language statistics, working memory, individual differences. Statistics: Alternatives to null hypothesis testing, effect sizes and confidence intervals, multigroup confirmatory factor analysis, item response theory, Bayesian inference statistics, hierarchical linear modeling, exploratory factor analysis, statistical reporting Check out my research statement. Experience The DOOM Lab (2019-Current) Harrisburg University of Science and Technology, Analytics Principal Investigator The DOOM Lab (2010-2018) Missouri State University, Department of Psychology Principal Inves... | |
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dm13450.github.io
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| | | | | An autoregressive process can be described by the equation | |
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hbiostat.org
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| | | | | Continuous learning from data and computation of probabilities that are directly applicable to decision making in the face of uncertainty are hallmarks of the Bayesian approach. Bayesian sequential designs are the simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with complexities if not doing the analysis at a single fixed time. Bayesian posterior probabilities, on the other hand, can be computed at any point in the trial and provide current eviden... | |
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sebastianraschka.com
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| | | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs). | ||
