View hierarchical_partial_pooling.ipynb
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View BMI Trajectories.ipynb
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View Homework 2 solution.ipynb
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View HME Trial Preliminary Analyses.ipynb
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View Key Question 3 Meta-analysis.ipynb
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View neff.py
def effective_n(trace_dict):
def get_vhat(x):
# number of chains is last dim (-1)
# chain samples are second to last dim (-2)
num_samples = x.shape[-2]
# Calculate between-chain variance
B = num_samples * np.var(np.mean(x, axis=-2), axis=-1, ddof=1)
View Section6_2-Clustering.ipynb
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View Dual Endpoints.ipynb
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View Najwa Visit.ipynb
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View Beta_product.py
from pymc3 import Beta, Deterministic, Model, sample
with Model():
a = Beta('a', 3, 1)
b = Beta('b', 2, 5)
a_times_b = Deterministic('a_times_b', a*b)
trace = sample(2000)