# Priors for New Physics

@article{Pierini2011PriorsFN, title={Priors for New Physics}, author={Maurizio Pierini and Harrison B. Prosper and Sezen Sekmen and Maria Spiropulu}, journal={arXiv: Data Analysis, Statistics and Probability}, year={2011} }

The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian approach, requires the construction of multi-parameter priors. We propose a construction that uses elements of Bayesian reference analysis. Our idea is to initiate the chain of inference with the reference prior for a likelihood function that depends on a single parameter of interest that is a function of the parameters of the physics model. The reference posterior density of the parameter of…

## Figures and Tables from this paper

## 3 Citations

Reference analysis of the signal + background model in counting experiments II. Approximate reference prior

- Mathematics
- 2012

The objective Bayesian treatment of a model representing two independent Poisson processes, labelled as ``signal'' and ``background'' and both contributing additively to the total number of counted…

Statistical inference for periodic and partially observable Poisson processes

- Computer Science
- 2019

This thesis develops practical Bayesian estimators and exploration methods for count data collected by autonomous robots with unreliable sensors for long periods of time. It addresses the problems of…

## References

SHOWING 1-10 OF 25 REFERENCES

Bayesian methods for adaptive models

- Computer Science
- 1992

The Bayesian framework for model comparison and regularisation is demonstrated by studying interpolation and classification problems modelled with both linear and non-linear models, and it is shown that the careful incorporation of error bar information into a classifier's predictions yields improved performance.

The Bayesian choice : from decision-theoretic foundations to computational implementation

- Computer Science
- 2007

This paperback edition, a reprint of the 2001 edition, is a graduate-level textbook that introduces Bayesian statistics and decision theory and is a worthy successor to DeGroot's and Berger's earlier texts.

The Theory of Probability

- Computer Science
- 1896

This book is a searching analysis of the fundamental principles of the theory of probability and of the particular judgments involved in its application to concrete problems and is in agreement with the views expressed by Dr. Wrinch and the present reviewer.

Ann

- Psychology
- 2005

Aaron Beck’s cognitive therapy model has been used repeatedly to treat depression and anxiety. The case presented here is a 34-year-old female law student with an adjustment disorder with mixed…

arXiv:1007.3897 [hep-ph]

- JHEP 0410,
- 2004

Phys

- Rev. D 82, 034002
- 2010

Izvestiya Fiziko-matematicheskogo obschestva pri Kazanskom universitete

- 2-ya seriya, tom 15, 135 (1906); A. A. Markov, reprinted in Appendix B of R. Howard, Dynamic Probabilistic Systems, Vol. 1: Markov Chains (John Wiley and Sons, 1971). For a modern textbook introduction see, for example, B. A. Berg, Markov Chain Monte Carlo Simulations And Their Statistical Analysis
- 2004

Particle Data Group Collaboration

- J. Phys. G G37,
- 2010

Bayesian Methods for Adaptive Models

- Computer Science
- 2011

The Bayesian framework for model comparison and regularisation is demonstrated by studying interpolation and classification problems modelled with both linear and non–linear models and it is shown that the careful incorporation of error bar information into a classifier’s predictions yields improved performance.