Posts Tagged ‘ Bayesian statistics ’

Item-response and ideal point models

April 15, 2015
By

To continue from today’s class, here’s what we’ll be discussing next time: - Estimating the direction and the magnitude of the discrimination parameters. - How to tell when your data don’t fit the model. - When does ideal-point modeling make a difference? Comparing ideal-point estimates to simple averages of survey responses. P.S. Unlike the previous […] The post Item-response and ideal point models appeared first on Statistical Modeling, Causal Inference,…

Read more »

Conflict of interest

April 13, 2015
By
Conflict of interest

Disclaimer: I'm fully aware of the obvious conflict of interest here, but also I think that this looks really good, so I'll write about it anyway.This post is to highlight that Marta's and Michela's book on Spatial and Spatio-temporal Bayesian Mod...

Read more »

Why do we communicate probability calculations so poorly, even when we know how to do it better?

April 13, 2015
By

Haynes Goddard writes: I thought to do some reading in psychology on why Bayesian probability seems so counterintuitive, and making it difficult for many to learn and apply. Indeed, that is the finding of considerable research in psychology. It turns out that it is counterintuitive because of the way it is presented, following no doubt […] The post Why do we communicate probability calculations so poorly, even when we know…

Read more »

Summer Internship at Novartis: Stan PK/PD Modeling

April 11, 2015
By
Summer Internship at Novartis:  Stan PK/PD Modeling

This looks like a great way to spend a summer: Summer Internship at Novartis Integrated Quantitative Sciences Here’s the job description: Bayesian modeling tools with Stan: Create re-usable tools for the Bayesian modeling of pharmacometrics data that can integrate diverse data sources (including pre-clinical, in-silico model predictions, etc.). Using the latest Stan’s facilities (http://mc-stan.org) develop […] The post Summer Internship at Novartis: Stan PK/PD Modeling appeared first on Statistical Modeling,…

Read more »

New research in tuberculosis mapping and control

April 9, 2015
By

Mapping and control. Or, as we would say, descriptive and causal inference. Jon Zelner informs os about two ongoing research projects: 1. TB Hotspot Mapping: Over the summer, I [Zelner] put together a really simple R package to do non-parametric disease mapping using the distance-based mapping approach developed by Caroline Jeffery and Al Ozonoff at […] The post New research in tuberculosis mapping and control appeared first on Statistical Modeling,…

Read more »

Comparison of Bayesian predictive methods for model selection

April 7, 2015
By

This post is by Aki We mention the problem of bias induced by model selection in A survey of Bayesian predictive methods for model assessment, selection and comparison, in Understanding predictive information criteria for Bayesian models, and in BDA3 Chapter 7, but we haven’t had a good answer how to avoid that problem (except by […] The post Comparison of Bayesian predictive methods for model selection appeared first on Statistical…

Read more »

But when you call me Bayesian, I know I’m not the only one

April 6, 2015
By
But when you call me Bayesian, I know I’m not the only one

Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the […] The post But when you call me Bayesian, I know I’m not the only…

Read more »

House of stats

March 31, 2015
By
House of stats

[This is a rather long joint post with Roberto Cerina and compounds our paper in the April 2015 issue of Significance]1. Prelude (kind-of unrelated to what follows). Last week, Marta and I finished watching the last series of House of Cards, the N...

Read more »

Regression: What’s it all about? [Bayesian and otherwise]

March 29, 2015
By

Regression: What’s it all about? Regression plays three different roles in applied statistics: 1. A specification of the conditional expectation of y given x; 2. A generative model of the world; 3. A method for adjusting data to generalize from sample to population, or to perform causal inferences. We could also include prediction, but I […] The post Regression: What’s it all about? [Bayesian and otherwise] appeared first on Statistical…

Read more »

The publication of one of my pet ideas: Simulation-efficient shortest probability intervals

March 28, 2015
By

In a paper to appear in Statistics and Computing, Ying Liu, Tian Zheng, and I write: Bayesian highest posterior density (HPD) intervals can be estimated directly from simulations via empirical shortest intervals. Unfortunately, these can be noisy (that is, have a high Monte Carlo error). We derive an optimal weighting strategy using bootstrap and quadratic […] The post The publication of one of my pet ideas: Simulation-efficient shortest probability intervals…

Read more »


Subscribe

Email:

  Subscribe