I quote the whole related documents about Bayesian theory, and I am confused by a specific part "from the Bayesian viewpoint there is only a single data set D (namely the one that is actually observed)". My question is, suppose we have a few choices for values of parameter `w`

, but we only have one observation of data set `D`

, and if data set `D`

is limited and bias, there is possible no co-existence of some possible value of `w`

and `D`

together, when how do we calculate in such case `P(D|w)`

? Thanks.

For example, if we want to calculate `P(D = win lottery | w = women)`

and in observed data set `D`

, there is no data about women, how do we going to do? Treating it as zero posterior (`P(w = woman| D = win lottery)`

) seems not very perfect?

regards,

Lin

**Contents**hide

#### Best Answer

Using Bayes theorem is **not the same** as using Bayesian statistics. You are mixing two different things.

*If you knew* what is the conditional probability of person's gender given his luck in lottery $Pr(text{gender} mid text{win})$ and the unconditional probability distribution of winning $Pr(text{win})$, then you could apply Bayes theorem to compute $Pr(text{win} mid text{gender})$. Notice that I did not use anywhere here terms such as *prior*, *likelihood*, or *posterior*, since they have nothing to do with such problems. (You could use naive Bayes classifier for such problems, but first is is not Bayesian since it does not use priors, and second you have insufficient data for it.)

As your quote mentioned, in Bayesian approach we have *prior*, *likelihood* and *posterior*. *Likelihood* is a conditional distribution of data given some parameter. *Prior* is distribution of this parameter that you *assume a priori* before seeing the data. *Posterior* is the estimate given the data you have and your *prior*.

To give concrete example illustrating it, let's assume that you have data about some coin since you threw it once and observed a head, let's call it $X$. Obviously, $X$ follows Bernoulli distribution parametrized by some parameter $p$ that is unknown and we want to estimate it. We do not know what is $p$, but we have *likelihood* function $f(X mid p)$, that is probability mass function of Bernoulli distribution over $X$ parametrized by $p$. To learn about $p$ Bayesian way, we assume *prior* for $p$. Since we have no clue what $p$ could be, we can decide to use weekly informative "uniform" Beta(1,1) prior. So out model becomes

$$ X sim mathrm{Bernoulli}(p) \ p sim mathrm{Beta}(alpha, beta) $$

where $alpha = beta = 1$ are parameters of beta distribution. Since beta is conjugate prior for Bernoulli distribution, we can easily compute the posterior distribution of $p$

$$ p sim mathrm{Beta}(alpha + 1, beta) $$

and it's expected value

$$ E(p mid X) – frac{alpha + 1}{alpha + beta + 1} = 0.66 $$

so given the data we have and assuming Beta(1,1) prior, expected value of $p$ is $0.66$.

### Similar Posts:

- Solved – Update samples of a Beta with Bernoulli likelihood to the Beta posterior
- Solved – Bayesian Statistics -Prior and Posterior distributions
- Solved – Beta distribution parameter estimation: method of moments
- Solved – Bayes estimator of Bernoulli random variables
- Solved – Bayes estimator of Bernoulli random variables