  Information geometry is the study of statistical models from a Riemannian
geometric point of view. The Fisher information matrix plays the role of a
Riemannian metric in this framework. This tool helps us obtain Cram\'{e}r-Rao
lower bound (CRLB). This chapter summarizes the recent results which extend
this framework to more general Cram\'{e}r-Rao inequalities. We apply Eguchi's
theory to a generalized form of Czsisz\'ar $f$-divergence to obtain a
Riemannian metric that, at once, is used to obtain deterministic CRLB, Bayesian
CRLB, and their generalizations.
