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import matplotlib.pyplot as plt | |
import numpy as np | |
import pandas as pd | |
import seaborn as sns | |
from theano import shared | |
import scipy.stats as stats | |
from scipy.stats import gamma, norm | |
import pymc3 as pm | |
import theano.tensor as tt | |
import arviz as az | |
#Make up fake data | |
mu = 3 | |
sd = 0.5 | |
n_columns = 5 | |
n_drops = 1000 | |
D = norm(mu, sd) | |
droplet_sizes = D.rvs(n_drops,n_columns) | |
## duplicate the same droplet sizes to create second set of data | |
droplet_sizes = np.tile(droplet_sizes,(2,1)) | |
n = 2. # what I am trying to solve for | |
active = droplet_sizes > mu + n * sd | |
kill = np.multiply(droplet_sizes, active) | |
kill = np.sum(kill, axis=1) | |
kill = kill.reshape(2,1) | |
with pm.Model() as model_b: | |
#Priors | |
tau = pm.Normal('tau', mu = 2, sd=2) | |
ϵ = pm.HalfCauchy('ϵ', 5) | |
#Observed | |
active_ = droplet_sizes > mu + tau * sd | |
# Likelihood | |
μ = pm.Deterministic('μ', (droplet_sizes*active_).sum(axis=1)) | |
kill_pred = pm.Normal('kill_pred', mu=μ, sd=ϵ, observed=kill) | |
trace_b = pm.sample(init='advi',cores=5) | |
pm.traceplot(trace_b); |
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