Pengaplikasian Elaborated Likelihood Model dalam Strategi Komunikasi Kampanye “Ingat Pesan Ibu”

Medical crowdfunding in social media is growing to be a convenient, accessible, and secure manner to cover medical expenses. It differs from traditional donation initiatives and medical crowdfunding on non-social media platforms in that projects are disseminated via social media network and among acquaintances. Through semi-structured in-depth interviews on donation behaviors of 52 respondents, this study uses grounded theory to extract seven main categories that affect medical crowdfunding donation behavior in social media, namely interpersonal relationship, reciprocity of helping, attitude toward donation, perceived behavior control, perceived trust, project information, and characteristics of patients. In the spirit of Elaboration Likelihood Model, we develop a theoretical framework that the seven factors influence donation behavior in medical crowdfunding in social media via a central and a peripheral route.

Download Full-text

Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter

Optical Engineering ◽

10.1117/1.3465563 ◽

2010 ◽

Vol 49 (7) ◽

pp. 077004 ◽

Cited By ~ 3

Author(s):

Yihua Tan

Keyword(s):

Particle Filter ◽

Visual Tracking ◽

Likelihood Model

Download Full-text

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽

10.1190/1.2235858 ◽

2006 ◽

Vol 71 (5) ◽

pp. C81-C92 ◽

Cited By ~ 17

Author(s):

Helene Hafslund Veire ◽

Hilde Grude Borgos ◽

Martin Landrø

Keyword(s):

Stochastic Model ◽

Seismic Data ◽

Synthetic Data ◽

Time Lapse ◽

Bayesian Framework ◽

Reservoir Modeling ◽

Data Set ◽

The North ◽

Fluid Saturation ◽

Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Download Full-text