vlavorini

from math import lgamma
from numba import jit

@jit
def h(a, b, c, d):
    num = lgamma(a + c) + lgamma(b + d) + lgamma(a + b) + lgamma(c + d)
    den = lgamma(a) + lgamma(b) + lgamma(c) + lgamma(d) + lgamma(a + b + c + d)

    return np.exp(num - den)

vlavorini

from scipy.stats import beta
import numpy as np
from calc_prob import calc_prob_between

#This is the known data: imporessions and conversions for the Control and Test set
imps_ctrl,convs_ctrl=16500, 30 
imps_test, convs_test=17000, 50

#here we create the Beta functions for the two sets
a_C, b_C = convs_ctrl+1, imps_ctrl-convs_ctrl+1

vlavorini

from math import lgamma
from numba import jit

#defining the functions used
@jit
def h(a, b, c, d):
    num = lgamma(a + c) + lgamma(b + d) + lgamma(a + b) + lgamma(c + d)
    den = lgamma(a) + lgamma(b) + lgamma(c) + lgamma(d) + lgamma(a + b + c + d)
    return np.exp(num - den)

vlavorini

from mpmath import betainc

p=betainc(a_T, b_T, 0.003,1, regularized=True) #result: 0.48112566853812544

vlavorini

import seaborn as sns
import pandas as pd
import numpy as np
imps_ctrl,convs_ctrl=16500, 30 
imps_test, convs_test=17000, 50

#here we create the Beta functions for the two sets
a_C, b_C = convs_ctrl+1, imps_ctrl-convs_ctrl+1
a_T, b_T = convs_test+1, imps_test-convs_test+1

vlavorini

import numpy
from theano import shared
import pymc3 as pm

x = np.arange(len(df))

# we set a shared tensor useful for inference
x_shared = shared(x)

y = df['totale_casi'].values