Yet informed guesses, denoted p ~ i, k, supported by the literature or by preliminary surveys, can be used to calculate the sCFR expected value, denoted s CFR ~ = ∏ i = 1 N k p ~ i, k, and the sample sizes, denoted n ~ i, k, that optimize estimator precision: (9) n ~ i, k ≈ C c i, k c i, k (1 / p ~ i, k − 1 ) ∑ j = 1 N k c j, k (1 / p ~ j, k − 1 ), ∀ i = 1, …, N k.
A recent theoretical paper in Brain (Edwards et al., 2012) provides a nice example of this: it describes how one can understand functional (hysterical) symptoms as aberrant inference that follows from a failure to optimize precision (dopaminergic neuromodulation).
Variations of the technique have been evaluated to optimize precision with consideration of procedural ease.
Extensive experimentation was required to optimize precision, interpolation order, interpolation mode, table sizes, and simulation quality.
With the use of robotic sample preparation to optimize precision, this assay should be adaptable to clinical environments.
For that, we employed a similarity score threshold that optimized the enrichment and precision for drug-disease pairs with common or related molecular mechanisms (distance 0 or 1) by means of Pareto optimization [ 46].
We employed the Pareto functionality provided by KNIME [ 39] to obtain a phenotypic similarity score threshold that optimizes the enrichment and precision for drug-disease pairs with a shortest distance of 0 and 1.
Hohwy explores the idea that mechanisms for optimizing precision expectations map onto those that account for attention, and argues that attentional phenomena such as change blindness can be explained within the PC paradigm.
This paper presents a high-performance architecture for spiking neural networks that optimizes data precision and streaming of configuration data stored in main memory.
