# Is the Jacobian term needed if the prior is on the transformation parameter?

Suppose I have a strictly positive parameter $$\sigma$$ and I need to estimate it using the random walk Metropolis-Hasting algorithm.

I know that I can do a parameter transform, i.e., $$\beta=log(\sigma)$$ and estimate $$\beta$$ instead. It is more convenient for me to assume that the prior for $$\beta$$ follows a normal distribution, e.g., $$\beta ~ N(\alpha,\lambda)$$. ($$\alpha$$ and $$\lambda$$ are actually parameters from other parts of my model). Then the log likelihood for the prior is,

$$logprior= N(\beta,\alpha,\lambda,loglike=true)$$

My confusion is whether I should add the Jacobian term or not. Suppose I still need to add the Jacobian term, should I just make the log of the prior look like this?

$$logprior= N(\beta,\alpha,\lambda,loglike=true) + \beta$$

Is $$\beta$$ the correct Jacobian term? I deduced it from this answer, but I don't know if I understand it correctly.

• If you’re imposing a prior on the transformed parameter, you don’t need the Jacobian Commented Mar 26, 2022 at 2:39

The Jacobian is used when you convert back and forth between a distribution for $$\sigma$$ and a distribution for $$\beta$$. This is true of the conversion whether you are talking about the prior or posterior distribution. You don't need to add it when you are dealing with a posterior computation for a given parameter that is not changing. So, if you are making an inference about $$\beta$$, you can derive its posterior distribution without any use of the Jacobian, as:

$$\pi(\beta|\mathbf{x}) \propto L_\mathbf{x}(\beta) \cdot \pi(\beta).$$

Similarly, if you are making an inference about $$\sigma$$, you can derive its posterior distribution without any use of the Jacobian, as:

$$\pi'(\sigma|\mathbf{x}) \propto L_\mathbf{x}'(\sigma) \cdot \pi'(\sigma).$$

(I'm using the primes here to reflect the fact that these sets of functions are different from each other, since they are for different paramters.) Since $$\beta = \log \sigma$$ these functions are related by:

\begin{align} L_\mathbf{x}'(\sigma) &\overset{\sigma}{\propto} L_\mathbf{x}(\log \sigma), \\[12pt] \pi'(\sigma) &\overset{\sigma}{\propto} \pi(\log \sigma) \cdot \frac{1}{\sigma}, \\[12pt] \pi'(\sigma|\mathbf{x}) &\overset{\sigma}{\propto} \pi'(\sigma|\mathbf{x}) \cdot \frac{1}{\sigma}, \\[12pt] L_\mathbf{x}(\beta) &\overset{\beta}{\propto} L_\mathbf{x}'(e^\beta), \\[12pt] \pi(\beta) &\overset{\beta}{\propto} \pi'(\exp \beta) \cdot e^\beta, \\[12pt] \pi(\beta|\mathbf{x}) &\overset{\beta}{\propto} \pi'(e^\beta|\mathbf{x}) \cdot e^\beta. \\[12pt] \end{align}

As you can see, the Jacobian enters into these relationships when we convert either the prior or posterior distribution for one parameter to the other.