scholarly journals Statistical connections on decomposable Riemann manifold

2020 ◽  
Vol 5 (5) ◽  
pp. 4722-4733
Author(s):  
Cagri Karaman ◽  
Keyword(s):  
2006 ◽  
Vol 58 (12) ◽  
pp. 1818-1833
Author(s):  
V. G. Bondarenko

Author(s):  
Vassilios Stathopoulos ◽  
Mark A. Girolami

Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.


Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1583
Author(s):  
Jong Taek Cho

We prove that a contact strongly pseudo-convex CR (Cauchy–Riemann) manifold M2n+1, n≥2, is locally pseudo-Hermitian symmetric and satisfies ∇ξh=μhϕ, μ∈R, if and only if M is either a Sasakian locally ϕ-symmetric space or a non-Sasakian (k,μ)-space. When n=1, we prove a classification theorem of contact strongly pseudo-convex CR manifolds with pseudo-Hermitian symmetry.


Sign in / Sign up

Export Citation Format

Share Document