Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone ...
Explore 10,000 academic publications
Summary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone ...
In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. ...
The base isolation design usually used the historical well-known earthquake records as an input ground motion. Through the adjustment on each variables of the structure system, ...
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Sca...
This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choice...
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (wher...
Requirements are an integral part of industry operation and projects. Not only do requirements dictate industrial operations, but they are used in legally binding contracts betw...
ABSTRACT– A self‐assessment scale has been developed and found to be a reliable instrument for detecting states of depression and anxiety in the setting of an hospital medical o...
We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting,...