Description
* Overview
This homework is about working with the beta–binomial unigram
language model. The purpose of this homework is threefold:
* To understand some of the properties of the gamma function,
including its role in the beta–binomial unigram language model.
* To explore the effects of the parameters (i.e., the concentration
parameter and mean) of the beta distribution.
* To understand and implement the probabilistic generative process
for the beta–binomial unigram language model.
To complete this homework, you will need to use Python 2.7 [1], NumPy
[2], and SciPy [3]. Although all three are installed on EdLab, I
recommend you install them on your own computer. I also recommend you
install and use IPython [4] instead of the default Python shell.
Before answering the questions below, you should familiarize yourself
with the code in hw2.py. In particular, you should try running
sample(). For example, running ‘sample(array([5, 2, 3]), 10)’
generates 10 samples from the specified distribution as follows:
>>> sample(array([5, 2, 3]), 10)
array([0, 0, 2, 2, 1, 0, 0, 0, 2, 1])
* Questions
** Question 1
The gamma function, which plays an important role in the normalization
constant of the beta distribution, is defined as follows:
Gamma(x) = int_0^inf du e^{-u} * u^{x – 1}.
a) Show that Gamma(1) = 1.
b) Use integration by parts to show that Gamma(x + 1) = x * Gamma(x).
The normalization constant for the beta distribution involves the
following integral, known as the beta function:
B(x, y) = int_0^1 dt t^{x – 1} * (1 – t)^{y – 1}.
c) Show that B(x, y) = Gamma(x) * Gamma(y) / Gamma(x + y). Hint: you
should start with Gamma(x) = int_0^inf du e^{-u} * u^{x – 1} and
Gamma(y) = int_0^inf dv e^{-v} * v^{y – 1}. Hint: you will need to
use the change of variable u = zt and v = z * (1 – t).
** Question 2
The PDF for the beta distribution is defined as follows:
Beta(x | beta, n_1) = (Gamma(beta) / (Gamma(beta * n_1) * Gamma(beta *
(1 – n_1)))) * x^{beta * n_1 – 1} * (1 – x)^{beta * (1 – n_1) – 1}
a) Implement the PDF for the beta distribution by filling in the
missing code in beta_pdf(). Hint: you will need to use
scipy.special’s gamma() function [5].
Function plot_beta_priors() takes two arguments: a list of
concentration parameters and a list of means. For each pair in the
cartesian product of these lists, plot_beta_priors() plots the PDF for
the beta distribution parameterized by those values.
b) Use plot_beta_priors() to generate a plot of the PDFs for all
beta distributions with concentration parameters 0.1, 1, 2, 10,
and 100 and means 0.05, 0.25, 0.5, 0.75, and 0.95.
c) Describe the effect on the PDF for the beta distribution of
increasing the concentration parameter from 0.1 to 100.
** Question 3
Function sample() contains code for sampling from an unnormalized
discrete distribution using the inverse transform method [6].
a) Implement the generative process for the beta–binomial unigram
language model by filling in the missing code in
generate_corpus(). Hint: you will need to use sample() and
numpy.random’s beta() function [7]. You should pay special
attention to the parameterization of the beta distribution used in
beta() as it differs from the parameterization used in class.
** Question 4
According to the beta–binomial unigram language model, the predictive
probability of a single new token w_{N + 1} of type ‘no’ (given
observed data D and assumptions H) is given by
P(w_{N + 1} = no | D, H) = (N_1 + beta * n_1) / (N + beta),
where N is the number of tokens in D, of which N_1 are of type ‘no’.
a) If D contains few observations (i.e., N is small), which term
will dominate the predictive probability?
b) What will the predictive probability tend toward as N increases?
c) Why can beta and beta * n_1 be interpreted as “pseudocounts” [8]?
** Question 5
According to the beta–binomial unigram language model, the evidence
for observed data D (consisting of N tokens, of which N_1 are of type
‘no’ and N – N_1 are of type ‘yes’) given assumptions H is given by
P(D | H) = (Gamma(beta) * Gamma(N_1 + beta * n_1) * Gamma(N – N_1 +
beta * (1 – n_1))) / (Gamma(N + beta) * Gamma(beta * n_1) *
Gamma(beta * (1 – n_1)),
where beta and n_1 are hyperparameters.
a) Use this definition, along with the definition of P(w_{N+1} = no |
D, H) given in question 4, the recurrence Gamma(x + 1) = x *
Gamma(x), and the definition of the chain rule [9] to show how P(D
| H), the evidence for observed data D given assumptions H, can
also be written without any gamma functions.
b) Explain how the predictive probability of new data D’ given
observed data D and assumptions H can be computed incrementally,
without any gamma functions, as each new token in D’ is observed.
* References
[1] http://www.python.org/
[2] http://numpy.scipy.org/
[3] http://www.scipy.org/
[4] http://ipython.org/
[5] http://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html#scipy.special.gamma
[6] http://en.wikipedia.org/wiki/Inverse_transform_sampling
[7] http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.beta.html#numpy.random.beta
[8] http://en.wikipedia.org/wiki/Pseudocount
[9] http://en.wikipedia.org/wiki/Chain_rule_%28probability%29
