[PDF] Top 20 Continuous inference for aggregated point process data
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Continuous inference for aggregated point process data
... spatially continuous point process model ...a data augmentation step for generating putative case locations at the sub- aggregation level using multinomial sampling (making use of additional ... See full document
33
Causal Inference in Discretely Observed Continuous Time Processes
... full continuous time treatment process is ...binary process and at the observational time points we are able to ask subjects about their treatment history ...the continuous time processes are ... See full document
186
Exact Bayesian inference for animal movement in continuous time
... of data on an increasingly wide range of ...such data has received considerable attention in recent years – for example Blackwell (2003); Morales et ...movement process of an animal as taking place ... See full document
13
Approximate Inference for Determinantal Point Processes
... mial, and minimizing an arbitrary polynomial, even subject to only polynomial constraints, is NP-hard. There do exist techniques for optimizing semi-definite re- laxations of polynomials, such as those summarized in ... See full document
164
Nonparametric directionality measures for time series and point process data
... and point process ...step point process signals can no longer be con- sidered as spike ...a continuous process which has a constant ...a continuous process with a ... See full document
26
Spatial point process models for MRI lesion data in multiple sclerosis
... joint process is simply given by π(w)λ(y) [Møller and Waagepetersen, ...marked point process model in the sense of having attached a categorical (type-specific) mark to each point ...mean ... See full document
183
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
... Gaussian process classification Hensman and Matthews (2015) consider Gaussian process classification with a pro- bit inverse link function and suggest a variational Gaussian model that builds on inducing ... See full document
8
Associations Between Gaussian Markov Random Fields and Gaussian Geostatistical Models with an Application to Model the Impact of Air Pollution on Human Health
... epidemiology, data con- cerning a spatial process of interest are often observed at different spatial ...pollution data and the health outcomes may be available as point- referenced ... See full document
83
Aggregated student confidence estimates support continuous quality improvements in a competencies-oriented curriculum
... longitudinal data may be used to design learning sciences principles-based faculty development for those responsible for poorly ranked PTALMs or, where necessary, to support an administrative decision to reassign ... See full document
7
Markov chain Monte Carlo methods for state space models with point process observations
... complete data likelihood (also known as the Q-function) was unimodal and highly nongaussian (skewed) with respect to its param- eters (Yuan & Niranjan, ... See full document
26
Gaussian process approximations for fast inference from infectious disease data
... Each of the Gaussian models previously described is chosen on the basis of different a priori assump- tions, however we would like to find a way to control the errors introduced in a systematic manner. Since we are ... See full document
24
Point process modelling of coordinate based meta analysis neuroimaging data
... There are three aspects of fMRI experiments that challenge the utility of individual studies. Firstly, fMRI studies su↵er from low power. The typical sample size of an fMRI study is small, and the majority of experiments ... See full document
182
Bayesian inference on stochastic gene transcription from flow cytometry data
... imaging data on gene expression (Featherstone et ...expression data from flow cytometry experiments such as FACS or FISH, which only report gene expression at a single point in ...chastic ... See full document
9
Joint Models for Spatial and Spatio-Temporal Point Processes
... model, data are assumed to arise as a mixture of a point mass at zero and a Poisson component with component membership for observations being ...cases aggregated by pub- lic health unit, the ... See full document
160
Supervised neural machine translation based on data augmentation and improved training & inference process
... the following changes: 1) We proposed data augmentation method (Yihan et al., 2018) and back translation algorithm (Sennrich et al., 2015), which was observed to be useful in Japanese- English and Japanese-Chinese ... See full document
5
A Structured Process for Transforming Usability Data into Usability Information
... Existing usability engineering tool support helps to improve the efficiency of experts, but does little to improve the effectiveness of novices. Experienced usability practitioners typically have developed methods and ... See full document
17
On the δ continuous fixed point property
... It is obvious that a space with the wcFPP has necessarily the 0-continuous fixed point property and a space with the 0-continuous fixed point property has both the FPP and the fixed poin[r] ... See full document
5
Exact Simulation of Jump Diffusion Processes with Monte Carlo Applications
... the continuous component builds on the recent Exact Algorithm ...the process’ trajec- tory, including discrete averages, max/min, crossing events, hitting ... See full document
32
Bayesian Inference on a Cox Process Associated with a Dirichlet Process
... The bayesian inference results are summarized in table 2 using pos- terior means, medians, standard deviations and 95% highest poste- rior density (HPD) intervals. The bayesian mean number of hidden events is ... See full document
7
Cross Sentence Inference for Process Knowledge
... tence inference method for automatically acquiring such role-based knowledge for new ...a process dataset shows that cross sentence inference using an Integer Linear Program helps im- prove the ... See full document
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