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I have got a set $\{Y_t\}$ of observations consisting of two subsets $\{Y_{t,1}\}$ and $\{Y_{t,2}\} \subset \{Y_t\}$ with $\{Y_{t,1}\} \sim \mathcal{N}(\mu_1,\sigma^2)$ and $\{Y_{t,2}\} \sim \mathcal{N}(\mu_2,\sigma^2)$ i.e. different means but the same variance (resulting from a regime switching model).

I know the means and want to draw a sample of $\sigma^2$ in a step of a MCMC estimation.

In the case of $\mu_1 = \mu_2$ I would have used the conjugate prior of the Inverse Gamma distribution (see [1], "Normal with known mean").

Can I use a conjugate prior in the case of $\mu_1 \neq \mu_2$ as well? For example by setting $\beta = \beta_0 + \frac{1}{2}\sum_{i=0}^n(Y_i - \mu_{I_i})^2 $ ($I_i$ being the correct indices according to the observations)?

Or will I have to use Metropolis-Hastings to get my sample of $\sigma^2$?

Best, Matt

[1] http://en.wikipedia.org/wiki/Conjugate_prior#Continuous_distributions

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  • $\begingroup$ This is a Gaussian linear model and there is a conjugate family of priors. The Jeffreys prior belongs to this family and this is the one we commonly use (and this is not the proposal of @DavidR) $\endgroup$ – Stéphane Laurent Feb 10 '14 at 13:41
  • $\begingroup$ Sorry for ignoring your answer, Stéphane, I unfortunately didn't see it until now. How would you apply the Jeffreys prior to my problem and in which ways would it be preferrable to the Inverse Gamma prior? $\endgroup$ – Matt Mar 3 '14 at 11:53
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Centering your set of ${Y_t}$ (i.e. ${Y_t^\ast} = {Y_t} - \mu_{I_i}$) has no influence on the variance. Therefore using centred data and a Inverse Gamma prior $IG(\alpha_0, \beta_0)$ results in an Inverse Gamma posterior with parameters $\alpha_0 + n/2$ and $\beta_0 + \frac{1}{2} \sum_{i=0}^n (Y_i - \mu_{I_i})^2$, which is equivalent to your idea (apart from the $^2$ you probably just forgot).

Aside from this, there is no need to use MCMC estimation when having a closed-form expression for the posterior. You can just draw from the above mentioned distribution - for example in R by using

alpha <- alpha0 + length(y_centred)/2
beta <- beta0 + sum((y_centred)^2)
sampleOfVar <- 1/rgamma(100,alpha,beta)
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  • $\begingroup$ Thank you, David. I am using MCMC as there is a second set of observations that is linked to the $Y_t$ by a function of $\sigma^2$. Therefore the sample from the conjugate-Inverse Gamma prior is used as an input for a Metropolis-Hastings step connecting this second set of observations. $\endgroup$ – Matt Feb 10 '14 at 13:16

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