This assignment involves the so-called change-point problem and a Bayesian analysis of it.

1. The simple change-point problem can be described as follows. Here it is assumed that both p₁(y) and p₂(y) are known completely.

• y₁, . . . , y­_τ|τ are independently identically distributed (iid) with distribution p₁(y)

           y_τ+1, . . . , y_n|τ, are iid as p₂(y)

           and τ takes values 1, . . . , n - 1

• If τ = 0 it is assumed that

           y₁, . . . , y_n|τ = 0, are iid p₂(y)

• If τ = n it is assumed that

           y₁, . . . , y_n|τ = n, are iid p₁(y)

The case τ = n corresponds to "no-change" and τ < n to "change".

(a) Find an expression for the posterior distribution of change-point for this simple model assuming the values τ = 0, 1, 2, . . . , n are allowed.

(b) Find the posterior distribution of the change-point for the British annual coal mining disasters data set for 1851 until 1962, available in the file coalminedata. R.

Assume that the distribution before the change is Poisson with mean 3.1 and after it is Poisson with mean 1.95.

Find the posterior distribution of the change-point and the mode of this distribution. What is an approximate 95% credible interval for τ? What is the posterior probability of "no-change"?

See the papers by Carlin et al, 1992, Hierarchical Bayesian Analysis of Change point Problems.

Applied Statistics.

Jarrett, 1979, A Note on the Intervals Between Coal-Mining Disasters. Biometrika.

2. (a) This question asks you to develop full conditional distributions for the Bayesian change-point problem which involves a change in mean but not variance of normally distributed data. Following on from Question 1, now take p1(y) to be the normal density with mean µ1 and precision (reciprocal variance) γ and p₂(y) to be the normal density with mean µ2 and precision (reciprocal variance) γ. Note the two distributions have the same precisions. The values of the parameters µ₁, µ₂, γ are all assumed unknown.

For τ taking the values 1, . . . , n - 1 (i.e. at least one observation from each of p₁ and p₂) find the likelihood p(y|µ₁, µ₂, γ, τ ) simplified to provide a computationally efficient formula  as a function of the parameters µ₁, µ₂, γ, τ . Assuming uniform uninformative priors for the parameters µ₁, µ₂, log(γ), τ , that is

p(µ₁, µ₂, γ, τ ) ∝ 1/γ, -∞ < µ₁ < ∞, -∞ < µ₂ < ∞, 0 < γ, τ ∈ {1, . . . , n - 1}

find the four full conditional posterior distributions:

p(µ₁|rest), p(µ₂|rest), p(γ|rest), p(τ|rest),

where "rest" means all the other parameters and the data y.

Describe a Gibbs sampling algorithm for generating the posterior distributions of the four unknown parameters.

(b) The data to be analysed involve a sequence of so-called temperature anomalies for North Russia at 20 year intervals, 1001, 1021, ..., until recently. Data is also available for various other sites in the world from about 800 AD until recently. The source is an IPCC report. Jansen E, J Overpeck, KR Briffa, J-C Duplessy, F Joos, V Masson-Delmotte, D Olago, B OttoBliesner, WR Peltier, S Rahmstorf, R Ramesh, D Raynaud, D Rind, O Solomina, R Villalba and D Zhang (2007) Palaeoclimate. In Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Solomon S, D Qin, M Manning, Z Chen, M Marquis, KB Averyt, M Tignor and HL Miller (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

The data are found in the file nrussia. R

Develop a Gibbs Sampling algorithm to find the posterior distribution of the change point using the model developed in Question 2(a). Report a 95% credible interval for the change-point and for the two parameters µ₁, µ₂.

Comment on whether the change-point model seems a reasonable model for these data.

3. (a) Suppose y₁, . . . y_n given θ are independent Poisson(θ) data so that the likelihood is

p(y|θ) = e^-nθθ^s/_j=1∏ⁿyj!                                 with s = _j=1Σⁿ y_j.

The marginal likelihood (or evidence) is given by

p(y) = ∫_θp(y|θ)p(θ)dθ.                                   (1)

Assuming that the prior for θ is given by a Gamma(α, β) distribution, show that the marginal likelihood, equation (1), is given by

p(y) = (1/_j=1∏ⁿy_j!) (β^α/Γ(α))(Γ(α + s)/(n + β)^α+s)                   where s = _j=1Σⁿy_j.

Show that the same result for p(y) is found by using the identity

p(y) = p(y|θ)p(θ)/p(θ|y).

(b) For two models M_j, j = 1, 2, we can compute the posterior odds of model M₁ to M₂ as

p(M₁|y)/p(M₂|y) = (p(y|M₁)/p(y|M₂))(p(M₁)/p(M₂)).

For Poisson data with mean θ, we want to compare M₁: θ = θ₀, with the value of θ₀ known, with M₂: 0 < θ < ∞ with θ having prior Gamma(α, β).

Here- p(y|Mj ) = ∫p(y|θ_j, M_j)p(θ_j|M_j) dθ_j                              j = 1, 2.

That is, (1) computed for M_j, j = 1, 2.

Assuming p(M₁) = p(M₂), find the posterior odds p(M₁|y)/p(M₂|y).

Assuming θ₀ = 1, compute this for s = n and s = 2n for n = 10(10)1000. Comment.

(c) For data y₁, . . . y_n, assume that the model with likelihood

p(y₁, . . . , y_n|θ₁, θ₂) = _j=1∏^tPoisson(y_j; θ₁) × _j=t+1∏ⁿPoisson(y_j; θ2)

and prior

p(θ₁, θ₂) = Gamma(θ₁; α₁, β₁) × Gamma(θ₂; α₂, β₂)

holds.

Describe in words what situations this probability model might represent.

Show that the marginal likelihood for this model is given by

(1/_j=1∏ⁿy_j!) x (β₁^α1/Γ(α₁))(Γ(α₁ + s_t)/(t + β₁)^α1+st) × (β₂^α2/Γ(α₂))(Γ(α₂ + s′_t)/(n - t + β₂)^α2+s′t)                (2)

using the results of Question 3(a) where s_t = _j=1Σ^t yj and s′_t = _j=t+1Σⁿy_j.

How can this expression, (2), be used to make inferences for the value of t if it is unknown (t = 1, . . . , n - 1).

Attachment:- russia and coalmine data.rar