human
Solve the following python programming problem given the following code:
# Hint

class LightBulb3(Suite, Joint):
    
    def Likelihood(self, data, hypo):
        lam, k = hypo
        x = data
        like = 1
        return like

# Solution goes here

from itertools import product

lams = np.linspace(0.001, 20, 101)
ks = np.linspace(0.001, 20, 101)

suite = LightBulb3(product(lams, ks))

suite.Update(1)

thinkplot.Contour(suite)

pmf_lam = suite.Marginal(0)
thinkplot.Pdf(pmf_lam)
pmf_lam.Mean()

pmf_k = suite.Marginal(1)
thinkplot.Pdf(pmf_k)
pmf_k.Mean()
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Note: based on this data alone, we can rule out some small values of lam and k, but we can't rule out large values.  Without more data or a more informative prior, the results are not useful.
To see why, try increasing the upper bounds in the prior distribition.
Left censored data
Exercise: Suppose you install a light bulb and then you don't check on it for a year, but when you come back, you find that it has burned out.  Extend LightBulb to handle this kind of data, too.