Consider adding this Baseball example in a notebook

[nabriis]

import pandas as pd
DATA_URL = "https://raw.githubusercontent.com/fonnesbeck/probabilistic_python/master/data/"
spin_rate_data = pd.read_csv(DATA_URL + "savant_data.csv", parse_dates=["game_date"]).dropna(subset=["spin_rate", "game_date"])

day_ind, date = pd.factorize(spin_rate_data.game_date, sort=True)
spin_rate = spin_rate_data.spin_rate.values
day_ind = xp.tensor(day_ind)

#spin_rate_data.head()
#spin_rate_data.plot.scatter(x="game_date", y="spin_rate", figsize=(14,5), alpha=0.2)

mu = Gaussian(xp.ones(2)*2500, 100)
tau = Uniform(0, 181)
sigma = HalfGaussian(100)
r = lambda tau, mu: xp.where(day_ind < tau, mu[0], mu[1])
sr = LogGaussian(r, lambda sigma: sigma)

samples = sample(mu, tau, sigma, sr, Ns=500, Nb=500, sr=spin_rate)