To illustrate the two direct modeling approaches for dealing with the measurement error inherent in cognitive test scores the MESE model and the use of PVs we begin with an example that uses data from the NALS.
This analysis provides a real data example that supports the results from the data simulations explored earlier, and the results suggest that the SNM approach outperforms the unsupervised approaches in this example.
But of course, rolling them all out to Chrome on tablets, phones, and desktops first (and potentially better integrating them in the case of the proxy example that apparently reduces data usage by 40%) is going to help adoption across devices - which must be a key aim for the company.
A hypothetical example that uses topographic data for the project area will be provided to demonstrate how WASH123D can help the design of a spreader swale system.
A BF of 10, for example, indicates that the data are 10 times more likely under one model than under the other.
We show, by analysis and example, that when the data contain significant information beyond what is assumed in the prior, the surrogate posterior can be very different from the posterior, and the resulting estimates become inaccurate.
It gave the example that the HESA data does not include "dormant" students who have stopped studying but have not formally left.
In the data center example, that means the attacker could find their way in, and attack a single machine, but couldn't replicate the attack across multiple machines.
Examples show that the data uncertainties may be of some significance for engineering applications.
