Think Bayes

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# Configure Jupyter so figures appear in the notebook
%matplotlib inline

# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'

import math
import numpy as np

from thinkbayes2 import Pmf, Suite, Joint
import thinkbayes2
import thinkplot


    

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class Battleship(Suite, Joint):
    lam = 1
    
    def Likelihood(self, hypo, data):
        x_actual, y_actual = hypo
        x_guess, y_guess, result = data
        d = np.hypot(x_guess-x_actual, y_guess-y_actual)
        p_hit = np.exp(-self.lam * d)
        return p if result == 'hit' else 1-p


    

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for t, p in gap.Items():
    arrivals = thinkbayes2.MakePoissonPmf(1.3 * t, 25)
    thinkplot.plot(arrivals, color='C0', linewidth=0.1)
    metapmf[arrivals] = p


    

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gap = thinkbayes2.MakeNormalPmf(7, 1, 3)
thinkplot.plot(gap)


    

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metapmf = thinkbayes2.Pmf()
for t, p in gap.Items():
    arrivals = thinkbayes2.MakePoissonPmf(1.3 * t, 25)
    thinkplot.plot(arrivals, color='C0', linewidth=0.1)
    metapmf[arrivals] = p


    

In [5]:

    

mix = thinkbayes2.MakeMixture(metapmf)
mix.Mean()
thinkplot.Hist(mix)
thinkplot.decorate(xlabel='Number of passengers',
                   ylabel='PMF')


    

In [6]:

    

mix[10]


    

    
Out[6]:





0.11021342082025383

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