bayesian-statistics

Hamilton’s Equations Suppose \(\mathbf{q} = \mathbf{q}(t) \in \mathbb{R}^d\) is a position vector of a particle at time \(t \geq 0\). We recall that \[\begin{align} \mathbf{v} &= \dfrac{\mathrm{d}\mathbf{q}}{\mathrm{d}t} \\ \mathbf{a} &= \dfrac{\mathrm{d}^2\mathbf{q}}{\mathrm{d}t^2} \end{align}\]

TL;DR my viewpoint I am in practice a Frequentist, but at heart a Bayesian. Regression To understand why I might take this perspective, let’s go through some theory. First, let’s assume a standard data-generating process for a regression:

Views and opinions expressed are solely my own. As I have been learning Bayesian statistics, one result that I have been somewhat aware of but have found it very difficult to find a proof of is the following statement: Jeffreys’ prior is invariant.