Posts tagged pymc3.Uniform

Lasso regression with block updating

Sometimes, it is very useful to update a set of parameters together. For example, variables that are highly correlated are often good to update together. In PyMC block updating is simple. This will be demonstrated using the parameter step of pymc.sample.

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Bayesian Estimation Supersedes the T-Test

Non-consecutive header level increase; H1 to H3 [myst.header]

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Using a “black box” likelihood function (numpy)

This notebook in part of a set of two twin notebooks that perform the exact same task, this one uses numpy whereas this other one uses Cython

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GLM: Robust Regression using Custom Likelihood for Outlier Classification

Using PyMC3 for Robust Regression with Outlier Detection using the Hogg 2010 Signal vs Noise method.

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Sequential Monte Carlo

Sampling from distributions with multiple peaks with standard MCMC methods can be difficult, if not impossible, as the Markov chain often gets stuck in either of the minima. A Sequential Monte Carlo sampler (SMC) is a way to ameliorate this problem.

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Hierarchical Partial Pooling

Suppose you are tasked with estimating baseball batting skills for several players. One such performance metric is batting average. Since players play a different number of games and bat in different positions in the order, each player has a different number of at-bats. However, you want to estimate the skill of all players, including those with a relatively small number of batting opportunities.

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GLM: Mini-batch ADVI on hierarchical regression model

Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that.

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