Discrete Data and the Multinomial Distribution

  • [1] (##) We consider IID data $D = \{x_1,x_2,\ldots,x_N\}$ obtained from tossing a $K$-sided die. We use a binary selection variable $$x_{nk} \equiv \begin{cases} 1 & \text{if $x_n$ lands on $k$th face}\\ 0 & \text{otherwise} \end{cases} $$ with probabilities $p(x_{nk} = 1)=\theta_k$.
    (a) Write down the probability for the $n$th observation $p(x_n|\theta)$ and derive the log-likelihood $\log p(D|\theta)$.
    (b) Derive the maximum likelihood estimate for $\theta$.
  • [2] (#) In the notebook, Laplace's generalized rule of succession (the probability that we throw the $k$th face at the next toss) was derived as $$\begin{align*} p(x_{\bullet,k}=1|D) = \frac{m_k + \alpha_k }{ N+ \sum_k \alpha_k} \end{align*}$$ Provide an interpretation of the variables $m_k,N,\alpha_k,\sum_k\alpha_k$.
  • [3] (##) Show that Laplace's generalized rule of succession can be worked out to a prediction that is composed of a prior prediction and data-based correction term.
  • [4] (#) Verify that
    (a) the categorial distribution is a special case of the multinomial for $N=1$.
    (b) the Bernoulli is a special case of the categorial distribution for $K=2$.
    (c) the binomial is a special case of the multinomial for $K=2$.
  • [5] (###) Determine the mean, variance and mode of a Beta distribution.
  • [6] (###) Consider a data set of binary variables $D=\{x_1,x_2,\ldots,x_N\}$ with a Bernoulli distribution $\mathrm{Ber}(x_k|\mu)$ as data generating distribution and a Beta prior for $\mu$. Assume that you make $n$ observations with $x=1$ and $N-n$ observations with $x=0$. Now consider a new draw $x_\bullet$. We are interested in computing $p(x_\bullet|D)$. Show that the mean value for $p(x_\bullet|D)$ lies in between the prior mean and Maximum Likelihood estimate.
  • [7] Consider a data set $D = \{(x_1,y_1), (x_2,y_2),\dots,(x_N,y_N)\}$ with 1-of-$K$ notation for the discrete classes, i.e., \begin{equation*} y_{nk} = \begin{cases} 1 & \text{if $y_n$ in $k$th class} \
      0 & \text{otherwise} 
    \end{equation} together with class-conditional distribution $p(x_n| y_{nk}=1,\theta) = \mathcal{N}(x_n|\mu_k,\Sigma)$ and multinomial prior $p(y_{nk}=1) = \pi_k$.
    (a) Proof that the joint log-likelihood is given by $$\begin{equation
    } \log p(D|\theta) = \sum{n,k} y{nk} \log \mathcal{N}(x_n|\muk,\Sigma) + \sum{n,k} y_{nk} \log \pi_k \end{equation}$$
    (b) Show now that the MLE of the
    class-conditional mean is given by $$\begin{equation} \hat \mu_k = \frac{\sumn y{nk} x_n}{\sumn y{nk}} \end{equation*} $$
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