Reliable ABC model choice via random forests

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Reliable ABC model choice via random forests

MOTIVATION Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. RESULTS We propose a novel approach based on a machine learning tool named random ...

متن کامل

ABC methods for model choice in Gibbs random fields

Gibbs random fields are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard model choice methods is hampered by the unavailability of the normalising constant in the Gibbs likelihood. In par...

متن کامل

Bayesian Model Choice using Coupled ABC

In Neal (2010), a novel Approximate Bayesian Computation (ABC) algorithm, coupled ABC, was introduced. This paper shows how coupled ABC can be used in an efficient manner for model choice in a Bayesian framework. The methodology is applied to Gibbs random fields and stochastic epidemic models. Furthermore a very efficient simulation procedure for Gibbs random fields with a given sufficient summ...

متن کامل

Lack of confidence in ABC model choice

Approximate Bayesian computation (ABC) have become an essential tool for the analysis of complex stochastic models. Grelaud et al. (2009, Bayesian Ana 3:427–442) advocated the use of ABC for model choice in the specific case of Gibbs random fields, relying on a inter-model sufficiency property to show that the approximation was legitimate. We implemented ABC model choice in a wide range of phyl...

متن کامل

Supervised Heterogeneous Domain Adaptation via Random Forests

Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domai...

متن کامل

ذخیره 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: Bioinformatics

سال: 2015

ISSN: 1460-2059,1367-4803

DOI: 10.1093/bioinformatics/btv684