Hi, I’m familiar with Bayesian updates using discrete data – but I’m confused on how to do the same thing for continuous data, and someone recommended PyMC3. Here’s my example:
My somewhat informative prior distribution of outcomes is this:
import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm prior = np.array([20.5,15.25,5.0,29.0,11.75,8.5,8.25,14.5,14.25, 23.25,31.75,44.5,9.75,2.75, 14.25, 7.0]) prior.sort() plt.plot(prior, norm.pdf(prior, prior.mean(), prior.std()))
And my observed evidence is:
evidence = np.array([27, 20.75, 24.5]
How would I update this very specific prior distribution, given 3 samples of evidence, using PyMC3? I would expect it to lower the density of outcomes below 20 and above 30 and increase the concentration between 20 and 30.
Thanks so much!
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