The entropy of H[y] is given by:
$$H[y] = - \int p_y(\mathbf{y})~ \ln p_y(\mathbf{y})~ d\mathbf{y}$$
Now, suppose that I want to make a linear transformation of vector $\mathbf{y}$ to change the variables of the integration to vector $\mathbf{x}$ according to:
$$\mathbf{y}=\mathbf{A}\mathbf{x}$$
Further, suppose that vector $\mathbf{x}$ is a continuous random variable with distribution $p_x(x)$ and corresponding entropy H[x].
What is H[y] in terms of H[x]?
Step 1:
$$H[\mathbf{y}] = - \int p_y(\mathbf{y})~ \ln p_y(\mathbf{y})~ d\mathbf{y}$$
Step 2: $$H[\mathbf{y}] = - \int \bigg(\frac{p_x(\mathbf{x})}{|\mathbf{A}|}\bigg) \ln \bigg( \frac{p_x(\mathbf{x})}{|\mathbf{A}|}\bigg)~ \bigg| \frac{\partial y}{\partial x} \bigg| d\mathbf{x}$$
Step 3: $$H[\mathbf{y}] = -\int p_x(\mathbf{x}) \ln \bigg(\frac{p_x(\mathbf{x})}{|\mathbf{A}|}\bigg)d\mathbf{x}$$
Step 4: $$H[\mathbf{y}] = -\int p_x(\mathbf{x}) \ln p_x(\mathbf{x}) d\mathbf{x} - \int p_x(\mathbf{x}) \ln\bigg( \frac{1}{|\mathbf{A}|}\bigg)d\mathbf{x}$$
Step 5: $$H[\mathbf{y}] = H[\mathbf{x}] + ln |\mathbf{A}|$$
My question is as follows:
What happened between step 1 and step 2? I wanted to see the details of this step broken down so that i could follow it. The other steps I can follow without any problems...