A BIRTH AND DEATH PROCESS FOR BAYESIAN NETWORK STRUCTURE INFERENCE
نویسندگان
چکیده
منابع مشابه
Calibrated Birth–Death Phylogenetic Time-Tree Priors for Bayesian Inference
Here we introduce a general class of multiple calibration birth-death tree priors for use in Bayesian phylogenetic inference. All tree priors in this class separate ancestral node heights into a set of "calibrated nodes" and "uncalibrated nodes" such that the marginal distribution of the calibrated nodes is user-specified whereas the density ratio of the birth-death prior is retained for trees ...
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 کاملDynamic Nonparametric Bayesian Models And the Birth-Death Process
When modeling longitudinal data using a set of hidden processes such as state-space models, a common assumption is that the number of hidden processes is fixed, and all hidden processes have the same life span (i.e., all start at the onset of the data stream and terminate at the end of the data stream). In this report I outline a framework of modeling complex longitudinal data using a birth-dea...
متن کاملBayesian Network Structure Inference with an Hierarchical Bayesian Model
Bayesian Networks (BNs) are applied to a wide range of applications. In the past few years great interest is dedicated to the problem of inferring the structure of BNs solely from the data. In this work we explore a probabilistic method which enables the inclusion of extra knowledge in the inference of BNs. We briefly present the theory of BNs and introduce our probabilistic model. We also pres...
متن کاملA Birth-Death Process for Feature Allocation
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birthdeath feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of N objects across a covariate (e.g. time) by creating and deleting features. A BDFP is exchangeable, projective, stationary and reversible, and its equilibrium distribution is given by the Ind...
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: Probability in the Engineering and Informational Sciences
سال: 2017
ISSN: 0269-9648,1469-8951
DOI: 10.1017/s0269964817000432