Design-based inference in time-location sampling
نویسندگان
چکیده
منابع مشابه
Sampling for Approximate Inference in Continuous Time Bayesian Networks
We first present a sampling algorithm for continuous time Bayesian networks based on importance sampling. We then extend it to continuous-time particle filtering and smoothing algorithms. The three algorithms can estimate the expectation of any function of a trajectory, conditioned on any evidence set constraining the values of subsets of the variables over subsets of the timeline. We present e...
متن کاملDesign Based Estimation of Finite Population Mean in Ranked Set Sampling
Abstract. This Article introduce method of ranked set sampling with replacement (RSSWR) in finite population and express how to computing samples of inclusion probability for this method. The Horvitz-Thompson and Hansen-Hurwtz estimators using auxiliary variables introduce for this design and use 2011-12 Urban Households Income and Income and Expenditure survey data, gathered for Tehran by stat...
متن کاملThe Analysis of Time-location Sampling Study Data*
Time-location sampling (TLS) is used to collect information from hard-to-reach populations by sampling persons at locations at which they may be found. Epidemiologic studies using TLS have often been analyzed ignoring both clustering within locations and the differential probabilities that persons are sampled. I propose a weighted analysis reflecting approximate differential sampling probabilit...
متن کاملFast Sampling-Based Inference in Balanced Neuronal Networks
Multiple lines of evidence support the notion that the brain performs probabilistic inference in multiple cognitive domains, including perception and decision making. There is also evidence that probabilistic inference may be implemented in the brain through the (quasi-)stochastic activity of neural circuits, producing samples from the appropriate posterior distributions, effectively implementi...
متن کاملNeuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability
It has been argued that perceptual multistability reflects probabilistic inference performed by the brain when sensory input is ambiguous. Alternatively, more traditional explanations of multistability refer to low-level mechanisms such as neuronal adaptation. We employ a Deep Boltzmann Machine (DBM) model of cortical processing to demonstrate that these two different approaches can be combined...
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: Biostatistics
سال: 2015
ISSN: 1465-4644,1468-4357
DOI: 10.1093/biostatistics/kxu061