In [1]:

```
import numpy as np
import matplotlib.pyplot as plt
import operator as op
import functools as ft
def summ(a_map):
"sum(map) does not do what one would expect it to do, but summ(map) does"
return ft.reduce(op.add, a_map)
def integr(acc, new):
"reduction function to compute an integral"
if not acc:
acc = [0]
last = 0
else:
last = acc[-1]
acc.append(last+new)
return acc
def pf(Proba):
"converting uniform draw into binary draw (factory function)"
def f(x):
return 0 if x>P else 1
f.__doc__ = "converts uniform draw into binary draw with proba %.4f" % Proba
return f
from scipy.misc import comb
def pdf(n,p,k):
"the binomial density function B(n,p,k)"
return comb(n, k) * p**k * (1-p)**(n-k)
```

In [2]:

```
def gaussf(mu=0.0,sig=1.0):
"factory function, returning an unnormalised Gaussian"
def _gaussf(x):
z = (x - mu)/sig
return exp(-z*z)
_gaussf.__doc__ = "unnormalised Gaussian (mu=%f, sig=%f)" % (mu,sig)
return _gaussf
def constf(c=1.0):
"factory function, returning an constant function"
def _constf(x):
return c + 0 * x
_constf.__doc__ = "constant function (c=%f)" % (c)
return _constf
def normalised(y, x):
"returns normalised y-vector (unity integral, over x; x assumed to be equally spaced)"
dx = x[1] - x[0]
s = sum(y) * dx
return y / s
#f = constf()
#g = gaussf()
#help(g)
```

In [3]:

```
class bayes2:
"""simple class allowing to compute Bayesian combination of two measurements
DESCRIPTION
the following properties are used to construct the data
min, max, n - support area (and grid steps)
mu1, sig1 - first measurement
mu2, sig2 - second measurement
title, xlabel, ylabel - graph labels
the function draw() then draws it
DEPENDENCIES
normalised()
gaussf()
constf()
"""
def __init__(self):
self.n = 1000
self.mu1 = 5
self.sig1 = 1
self.mu2 = 6
self.sig2 = 2
self.min = 0.
self.max = 10.
self.title = 'Measurements and Bayesian post density function'
self.xlabel = 'probability space location'
self.ylabel = 'probability density'
def draw(self):
x = np.linspace(self.min, self.max, 1000)
prior = constf(0.5)(x)
f1 = normalised(gaussf(self.mu1,self.sig1)(x),x)
f2 = normalised(gaussf(self.mu2,self.sig2)(x),x)
ff = normalised(f1 * f2,x)
#plt.plot(x,prior, label='prior')
plt.plot(x,f1, 'k--', label='m1')
plt.plot(x,f2, 'k--', label='m2')
plt.plot(x,ff, 'r-', label='post')
plt.title(self.title)
plt.xlabel(self.xlabel)
plt.ylabel(self.ylabel)
plt.legend()
plt.show()
b = bayes2()
```

We know that in a Bayesian setting the probability of our hypothesis conditional on our data is $$ P(H|D) \propto P(D|H) \times P(H) $$ where $P(H)$ is the prior, and $P(D|H)$ is the probability of our data conditional on the hypothesis.

What we are looking at now is a situation where we have two measurements for the same quantity, say two people measuring a length. One of the measurements is $\mu_1, \sigma_1$ (in the sense that the measurement we've got is $\mu_1$, and the measurement carries a Gaussian error of standard deviation $\sigma_1$), and the other one is $\mu_2, \sigma_2$.

In principle we are starting with a flat prior $P(H)$*, that we are then updating with the first measurement to obtain our new prior $P_1(H) = P(H|D1)$, that in turn we are updating with the second measurement. $$ P(H|D_1, D_2) \propto P(D_2|H) \times P_1(H) \propto P(D_2|H) \times P(D_1|H) \times P(H) $$ However, because the first prior is flat we can simply use $P(D_1|H)$ as our first and only prior.

*note: in fact we are starting not with a flat prior, but with a prior that has support $[min\ldots max]$ by virtue of the fact that we are not evaluating the functions outside of this area; this is one of the rather nice properties of this calculus: restricting the calculation area to an interval is a feature

In [4]:

```
b.mu1 = 5
b.sig1 = 1
b.mu2 = 5
b.sig2 = 1
b.draw()
```

Two equal measurements (same measurement and same error) lead to a more localised posterior function that is otherwise at the same place

In [4]:

```
```

Two measurements at the same location (same measurement but different error) lead to a posterior function that is at the same place, and thinner (or, at least as thin) as the thinner of the two

In [5]:

```
b.mu1 = 5
b.sig1 = 1
b.mu2 = 6
b.sig2 = 1
b.draw()
```

Two measurements of equal error but at different locations lead to a more localised function in the middle

In [6]:

```
b.mu1 = 3
b.sig1 = .5
b.mu2 = 7
b.sig2 = .5
b.draw()
```

Incongruent measurements lead to a rather pathological behaviour: in this case we have essentially two contradictory measurements of equal error, and they give a strong peak in the middle; this obviously makes sense when thinking about how those things are computed, but it is important to keep in mind that a single

overconfidentmeasurement can move things around dramatically

In [7]:

```
b.mu1 = 3
b.sig1 = .5
b.mu2 = 7
b.sig2 = 1
b.draw()
```

Similar effect as before, that incongruent measurements move the posterior distribution dramatically; note however that the more confident density is significantly stronger

In [8]:

```
b.mu1 = 3
b.sig1 = .5
b.mu2 = 7
b.sig2 = 2
b.draw()
```

Again a similar effect, but here the confident density mostly dominates the posterior

In [9]:

```
b.mu1 = 6
b.sig1 = .5
b.mu2 = 7
b.sig2 = 2
b.draw()
```

Here the two densities are congruent, but one is significantly more confident; the other one has very little impact

In [10]:

```
b.mu1 = 6
b.sig1 = .5
b.mu2 = 7
b.sig2 = 1
b.draw()
```

this is a scenario where the less confident density has some impact