2 more questions regarding Bayes Theorem 
I don't understand why my answer is wrong. My understand of likelihood is the P ( Data | model). For example, if the model is that a coin is fair, and the coin was flipped 10 times and came up heads 6 times, then the likelihood is (some binomial distribution math goes here... unsure what it is actually... I think it's $6 choose 10 * 0.5 ^ 6 * 0.5 ^ 4$. Is that right?)


  
*You go to Las Vegas and sit down at a slot machine. You are told by a highly reliable source that, for each spin, the probability of
  hitting the jackpot is either 1 in 1,000 or 1 in 1,000,000, but you
  have no prior information to tell you which of the two it is. You play
  ten times, but do not win the jackpot. What is the posterior
  probability that the true odds of hitting the jackpot are 1 in 1,000?
  

So is the math going to be:
$\frac{true-positive}{true-positive + false-positive}$
$\frac{(\frac{999}{1000}) ^{10} * \frac{1}{1000}}{(\frac{999}{1000}) ^{10} * \frac{1}{1000} + (\frac{999999}{1000000}) ^{10}}$
 A: Are you sure the first question is on Bayes theorem?  Did you attach the wrong screenshot?   Maybe i'm just not understanding it
This is what I get as the answer for the second question.  The prior likelihood for each of the two is 50% to start.  Then multiply each of them by the likelihood to fail 10 times  (999/1000  or 999999/1000000)   Then normalize

A: For the first problem, unless you are in a Bayesian course, we think that parameters are unknown fixed quantities and they don't have a 'distribution'. So I guess the answer is the last one. 
P.S. This problem is not designed properly because you might understand this but fails to catch what the professor wants to test you. In an epidemiology test I met multiple choice problem like "To monitor effects of an assigned exposure which one is the best" with choices such as 'cohort' and 'experimental' and I was deeply confused. Later I realized that this is a stupid English problem because of the word 'assigned'. 
You don't need to do stupid conceptual problem right to understand the concept of statistics, let alone to practice it. 
