Sampling random directions within an elliptical cone
نویسندگان
چکیده
منابع مشابه
Elliptical slice sampling
Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well...
متن کاملRandom packing of elliptical disks
The dense packing of hard objects is a recurrent paradigm in physics, for example in early models of crystallinity, and also in theories of granular materials which are under active debate today [1, 2]. Generally speaking the objects are taken to be spheres, leading to the formulation of the Kepler Problem (what is their closest packing?) and the investigations begun by Bernal on disordered pac...
متن کاملElliptical Slice Sampling with Expectation Propagation
Markov Chain Monte Carlo techniques remain the gold standard for approximate Bayesian inference, but their practical issues — including onerous runtime and sensitivity to tuning parameters — often lead researchers to use faster but typically less accurate deterministic approximations. Here we couple the fast but biased deterministic approximation offered by expectation propagation with elliptic...
متن کاملParallel MCMC with generalized elliptical slice sampling
Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them. Exact inference is frequently intractable, so approximate inference is often performed using Markov chain Monte Carlo (MCMC). To achieve the best possible results from MCMC, we want to efficiently simulate many steps ...
متن کاملGeneralizing Elliptical Slice Sampling for Parallel MCMC
Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them. Exact inference is frequently intractable, so approximate inference is often performed using Markov chain Monte Carlo (MCMC). To achieve the best possible results from MCMC, we want to efficiently simulate many steps ...
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: Computer Physics Communications
سال: 2017
ISSN: 0010-4655
DOI: 10.1016/j.cpc.2017.05.010