http://www.r-bloggers.com/introduction-to-bayesian-methods-guest-lecture/ Nice, gentle and viewable!

## Archive for October, 2012

### Intro to Bayesian methods

October 20, 2012### In continuation of the Normalizing Constant Paradox: The Harmonic Mean of the Likelihood: Worst Monte Carlo Method Ever | Radford Neal’s blog

October 14, 2012### Optimizing Survival Likelihoods With Poisson Models for Rate and Exposure

October 13, 2012In a previous post I mentioned that Poisson models can be used to carry out survival analysis tasks e.g. estimation of survival curves or even relative risk modelling. Yet, I never showed how this can be done. So I will close the gap today and highlight how this works from a purely mathematical vantage point.

### The Normalizing Constant Paradox | Normal Deviate

October 7, 2012An interesting question and an excellent discussion!

http://normaldeviate.wordpress.com/2012/10/05/the-normalizing-constant-paradox/

Any takers?

### Survival Analysis via Hazard Based Modeling and Generalized Linear Models

October 5, 2012The connection between survival analysis via hazard based modelling and generalized linear models had been made very early even since the description of the proportional hazard (PHM) Cox (1972) and generalized linear models (GLM) Nelder and Wedderburn (1972). For example,

– Breslow (1974) considers the proportional hazard model as a discrete time logistic regression in which discrete probability masses are put on the (ordered) set of observed failure times (more…)

### Who needs the Cox model anyway ?

October 4, 2012As provocative this title may read, it is hardly my creation and due credit should be given to Bendix Carstensen for giving numerous talks under this title and providing his teaching material and R source code for people to explore this point.

The title does neither imply that the Cox model is of no use at all, nor that one should abandon it even if only for the reason that the vast majority of biomedical literature makes use of it in one form of the other. Rather, it is meant to imply that one can put other more versatile regression models to the same uses and get out more out of the same data.