Example

1. Example • Human males have one X-chromosome and one Y-chromosome, while females have two X-chromosomes each chromosome being inherited from one parent. • Hemophilia is a disease that exhibits X-chromosome-linked recessive inheritance, meaning that a male who inherits the gene that cause the disease on the X-chromosome is affected, while a female carrying the gene on only one of her two X-chromosomes is not affected. The disease is generally fatal for woman who inherit two such genes, and this is very rare, since the frequency of occurrence of the gene is low in human populations. week 2

2. Consider a woman who has an affected brother, this implies that her mother must be a carrier of the hemophilia gene with one ‘good’ and one ‘bad’ X-chromosome. We are also told that her father is not affected; therefore the woman herself has a fifty-fifty chance of having the gene. The unknown quantity of interest is the state of the women and it had two values: the woman is either a carrier of the gene (θ=1) or not (θ=0). Based on the information provided so far, the prior distribution for the unknown θ can simply be expressed as Pr(θ = 1) = Pr(θ = 0) =½. • The data used to update this prior information consist of the affection status of the woman’s sons. Suppose she has two sons, neither of whom is affected. Let yi = 1 or 0 denote an affected or unaffected son respectively. We assume the two sons are not identical twins and so their outcomes conditional on θ are independent. The likelihood function is then… week 2

3. Bayes’ rule can be used to combine the information in the data with the prior distribution; in particular we are interested in the posterior probability that the woman is a carrier. This is given by… • Intuitively it is clear that if a woman has unaffected sons, it is less probable that she is a carrier, and Bayes’ rule provides a formal mechanism for determining the extent of the correction. week 2

4. Single-parameter models • We now consider four fundamental and widely used one-dimensional models. That is, models that have only a single scalar parameter. • These models are the binomial, normal, Poisson and exponential. week 2

5. Example: Bernoulli Model • Suppose we observe a sample from the Bernoulli(θ) distribution with unknown. • In this model the aim is to estimate an unknown population proportion from the result of a sequence of ‘Bernoulli trials’. • The likelihood function for this model is: • Suppose we choose the prior distribution on θ to be the Beta(α, β) distribution. • The posterior distribution is then… week 2

6. As a specific case, suppose we observe in a sample of n = 40 and that α = β = 1 (i.e. we have a uniform prior on θ). Then the posterior of θ is given by the Beta(11,31) distribution. The plot of the posterior and prior density looks like • The spread of the posterior distribution gives us some idea of how precise any probability statements we make about θ can be. Note how much information the data have added as reflected in the above graph. week 2

7. Example: Location Normal Model • Suppose that is a sample from an distribution where is unknown and is known. The likelihood function is given by… • Suppose we take the prior distribution of μ to be the for some specified choices of μ0 and . The posterior density is then proportional to… • Expending the above term we get that the posterior distribution of μ is the following normal distribution week 2

8. Note that the posterior mean is a weighted average of the prior mean μ0 and the sample mean. • Further, the posterior variance is smaller than the variance of the sample mean. So if the information expressed by the prior is accurate, inference about μ based on the posterior will be more accurate than those based on the sample mean alone. • Note, the more diffuse the prior is, i.e., the larger is, the less influence the prior has. week 2