Bayesian Inference Applied to Pulsar’s Models
نویسندگان
چکیده
منابع مشابه
Bayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملBayesian approach to inference of population structure
Methods of inferring the population structure, its applications in identifying disease models as well as foresighting the physical and mental situation of human beings have been finding ever-increasing importance. In this article, first, motivation and significance of studying the problem of population structure is explained. In the next section, the applications of inference of p...
متن کاملApplied Bayesian Inference in R using MCMCpack
Sampling) project is a long-running project to provide a user-friendly language and environment for Bayesian inference. The first article, by Andrew Thomas and colleagues, describes the BRugs package which provides an R interface to the OpenBUGS engine. The second article by Andrew Thomas describes the BUGS language itself and the design philosophy behind it. Somewhat unusually for an article i...
متن کاملBayesian inference applied to the electromagnetic inverse problem.
We present a new approach to the electromagnetic inverse problem that explicitly addresses the ambiguity associated with its ill-posed character. Rather than calculating a single "best" solution according to some criterion, our approach produces a large number of likely solutions that both fit the data and any prior information that is used. Whereas the range of the different likely results is ...
متن کاملBayesian Inference in densely connected networks applied to CDMA
Graphical models provide a powerful framework for modelling statistical dependencies between variables [1]. Message passing techniques are typically used for inference in graphical models that can be represented by a sparse graph. Iterative message passing is guaranteed to converge to the globally correct estimate when the system is tree-like; there are no such guarantees for systems with loops...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Modern Physics: Conference Series
سال: 2017
ISSN: 2010-1945
DOI: 10.1142/s2010194517600382