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You are here		www.countbayesie.com Working with Probability Distributions - Count Bayesie
\|	\|	geekyisawesome.blogspot.com Geeky is Awesome: Bernoulli vs Binomial vs Multinoulli vs Multinomial distributions	2.9 parsecs away Travel
\|	\|	Bernoulli distribution Say you are flipping a coin that has a probability of 0.4 of turning up heads and 0.6 of turning up tails. The simple...	2.9 parsecs away Travel
\|	\|	seeing-theory.brown.edu Seeing Theory - Probability Distributions	1.9 parsecs away Travel
\|	\|	A probability distribution specifies the relative likelihoods of all possible outcomes.	1.9 parsecs away Travel
\|	\|	jaketae.github.io A sneak peek at Bayesian Inference - Jake Tae	4.7 parsecs away Travel
\|	\|	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.	4.7 parsecs away Travel
\|	\|	www.nicktasios.nl Latent Diffusion Series: MNIST Classifier \| NICK TASIOS	17.3 parsecs away Travel
\|		In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In this first post, we will tr	17.3 parsecs away Travel