proof. wlog assume \(g\) is increasing (as a appropriate transformation function), then \begin{align} \text{pdf}Y(y) &= \frac{d}{dy}\text{cdf}_Y(y)\ &= \frac{d}{dy}\int_X(x)dx\ &= \frac{d}{dy} \text{cdf}_X(g^{-1}(y)) \ &= \text{pdf}_X(g^{-1}(y))|\frac{d}{dy}g^{-1}(y)| \end{align}^{g^{-1}(y)} \text{pdf}
Change of Variables for Multivariate Functions
Theorem For discrete random variables \(\mathbf X = (X_1,...,X_n)\), Let \(\mathbf G:\mathbb R^n\rightarrow\mathbb R^m\) be the transformation s.t. \(\mathbf Y = (Y_1,...,Y_m), \mathbf Y = \mathbf G(\mathbf X), Y_i = g_i(X_1, ..., X_n)\). Then
random variables \(X_1,...,X_n\) are said to be independent and identically distributed (iid.) if \(X_i\)'s are independent and have the same distribution.
Example
the sum of independent Bernoulli trails follows binomial distribution.
proof. Let \(X_i \sim \text{Bern.}(p)\). \(Y_n = \sum^n X_i\). We will prove by induction.
For random variables \(\mathbf X = (X_1,...,X_n)\), Let \(\mathbf G:\mathbb R^n\rightarrow\mathbb R^m\) be the transformation s.t. \(\mathbf Y = (Y_1,...,Y_m), \mathbf Y = \mathbf G(\mathbf X), Y_i = g_i(X_1, ..., X_n)\). IF \(\mathbf G\) is injective and differentiable, then