Bayesian Cell Force Estimation Introducing Cell Shape Prior
نویسندگان
چکیده
منابع مشابه
Introducing of Dirichlet process prior in the Nonparametric Bayesian models frame work
Statistical models are utilized to learn about the mechanism that the data are generating from it. Often it is assumed that the random variables y_i,i=1,…,n ,are samples from the probability distribution F which is belong to a parametric distributions class. However, in practice, a parametric model may be inappropriate to describe the data. In this settings, the parametric assumption could be r...
متن کاملBayesian integration in force estimation.
When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, t...
متن کاملAdaptive Bayesian Estimation via Block Prior
A novel block prior is proposed for adaptive Bayesian estimation. The prior does not depend on the smoothness of the function and the sample size. It puts sufficient prior mass near the true signal and automatically concentrates on its effective dimension. A rateoptimal posterior contraction is obtained in a general framework, which includes density estimation, white noise model, Gaussian seque...
متن کاملBayesian Subspace Estimation Using Sparse Promoting Prior
Hyperspectral sensors record the light intensity beyond the visible spectra in hundreds of narrow contiguous bands. Images are characterized by a high spectral resolution but a low spatial precision due to sensors constraints. A crucial step called unmixing consists of decomposing each pixel as a combination of pure spectra, called endmembers. Endmembers act as fingerprints, improving the abili...
متن کاملLearning Dynamical Shape Prior for Level Set based Cell Tracking
Automated cell tracking in populations is very crucial for studying dynamic cell cycle behaviors. However, a high accuracy of each step is essential to avoid error propagation. In this paper, we propose an integrated three-component system to tackle this problem. We first model the temporal dynamics of shape change using an autoregressive model, which is used for estimating the shape and the lo...
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: Biophysical Journal
سال: 2020
ISSN: 0006-3495
DOI: 10.1016/j.bpj.2019.11.2554