 # Python: Full Bayesian Predictive Distribution

Background Follow this link to download the full jupyter notebook. In one of the previous posts, we looked at the maximum likelihood estimate (MLE) for a linear regression model. Instead of using the deterministic model directly, we have also looked at the predictive distribution. In the previous post, we used this stochastic model to include information about the data uncertainty into the prediction process. When … Continue reading Python: Full Bayesian Predictive Distribution # Python: Predictive Distribution of the Least Square Estimate

If you want to see the code with syntax highlighting, download the gits for this post from my github. In the  previous post, we looked at the numerical calculation of the maximum likelihood estimate (MLE). As you might know, we can obtain the same solution in a much easier way using the method of least squares. It can be shown that solving for the maximum … Continue reading Python: Predictive Distribution of the Least Square Estimate Sticky post

# Python: Maximum Likelihood Estimate

In this post I want to talk about regression and the maximum likelihood estimate. Instead of going the usual way of deriving the least square (LS) estimate which conincides with the maximum likelihood (ML) under the assumption of normally distributed noise, I want to take a different route. Here, instead of using the analytical LS solution, I want to show you, how we can numerically … Continue reading Python: Maximum Likelihood Estimate