Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Bayesian estimation (1)
- Bias (1)
- Community resiliency (1)
- Continuity planning (1)
- Disaster preparedness (1)
-
- Disaster preparedness toolkit (1)
- Gibbs sampler (1)
- HMC-NUTS (1)
- Hamiltonian Monte Carlo-No-U-Turn-Sampler (1)
- Information first responders (1)
- Libraries (1)
- Morgridge College of Education (1)
- Public libraries (1)
- RMSE (1)
- Research Methods and Information Science (1)
- Research Methods and Statistics (1)
- Root mean squared errors (1)
- Safe haven (1)
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Education
Ports In A Storm: The Role Of The Public Library In Times Of Crisis, Michele Stricker
Ports In A Storm: The Role Of The Public Library In Times Of Crisis, Michele Stricker
Collaborative Librarianship
This article will provide you with guidance on how to prepare your library to respond a disaster, as well as, how to resume providing services to the public as quickly as possible in the aftermath of a local or regional crisis. You will learn how libraries contribute to community resiliency by providing a safe haven and needed services after a disaster that allow people and local businesses to begin to put their shattered lives back together and resume normal activities.
Recent natural and manmade catastrophes have highlighted the important role public libraries play in enhancing their community’s resiliency and post-disaster …
A Comparison Of Bayesian Estimation Techniques In A Multidimensional Two-Parameter Partial Credit Item Response Model, Peiyan Liu
Electronic Theses and Dissertations
Bayesian estimation methods have shown better performance than the traditional Marginal Maximum Likelihood (MML) estimation method for parameter estimation in relatively simple item response models. However, extant literature is lacking on the investigation of Bayesian parameter estimation approaches for a multidimensional two parameter partial credit (M2PPC) model, therefore this simulation study investigated the performance of two Bayesian Markov Chain Monte Carlo (MCMC) algorithms: Gibbs Sampler and Hamiltonian Monte Carlo-No-U-Turn-Sampler (HMC-NUTS) for M2PPC models' parameter estimation. It compared the estimation accuracy and computing speed in different combinations of situations, including prior choices, test lengths, and the relationships between dimensions.
The datasets …