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www.quantstart.com | ||
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jaketae.github.io
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| | | | | So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These distributions are interesting in and of themselves, but their true beauty shines through when we analyze them under the light of Bayesian inference. In today's post, we first develop an intuition for conditional probabilities to derive Bayes' theorem. From there, we motivate the method of Bayesian inference as a means of understanding probability. | |
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fharrell.com
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| | | | | This is the story of what influenced me to become a Bayesian statistician after being trained as a classical frequentist statistician, and practicing only that mode of statistics for many years. | |
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alexanderetz.com
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| | | | | [This post has been updated and turned into a paper to be published in AMPPS] Much of the discussion in psychology surrounding Bayesian inference focuses on priors. Should we embrace priors, or should we be skeptical? When are Bayesian methods sensitive to specification of the prior, and when do the data effectively overwhelm it? Should... | |
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www.v7labs.com
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| | | What are Generative Adversarial Networks and how do they work? Learn about GANs architecture and model training, and explore the most popular generative models variants and their limitations. | ||
