Posts tagged pymc3.HalfNormal

Using shared variables (Data container adaptation)

The pymc.Data container class wraps the theano shared variable class and lets the model be aware of its inputs and outputs. This allows one to change the value of an observed variable to predict or refit on new data. All variables of this class must be declared inside a model context and specify a name for them.

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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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Hierarchical Binomial Model: Rat Tumor Example

This short tutorial demonstrates how to use PyMC3 to do inference for the rat tumour example found in chapter 5 of Bayesian Data Analysis 3rd Edition [Gelman et al., 2013]. Readers should already be familliar with the PyMC3 API.

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Estimating parameters of a distribution from awkwardly binned data

Let us say that we are interested in inferring the properties of a population. This could be anything from the distribution of age, or income, or body mass index, or a whole range of different possible measures. In completing this task, we might often come across the situation where we have multiple datasets, each of which can inform our beliefs about the overall population.

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Multivariate Gaussian Random Walk

This notebook shows how to fit a correlated time series using multivariate Gaussian random walks (GRWs). In particular, we perform a Bayesian regression of the time series data against a model dependent on GRWs.

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Rolling Regression

Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.

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A Hierarchical model for Rugby prediction

Based on the following blog post: Daniel Weitzenfeld’s, which based on the work of Baio and Blangiardo.

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