For parameter learning of the semisupervised PPLS model, an efficient Expectation-Maximization algorithm is designed.
Using the proposed technique, we derive a computationally efficient expectation maximization (EM) estimator for simultaneous estimation of both SI and communication channels.
It decouples the inference in each dimension, and can be solved with an efficient expectation-maximization (EM) algorithm that avoids matrix inversion.
For training and parameter learning of the probabilistic PLS model, the Bayes rule is applied and an efficient Expectation-Maximization algorithm is designed.
First, we present an efficient Expectation-Maximization (EM -based algorithm for EM -basedg the malgorithmposteriori (MAP) parameters ofor PSG, givestimatingor paired-end RNA-Seq dathe
The crisis has exposed as worthless the predictions of new-classical economics – and, with them, its "state can do nothing" dogmas (efficient markets, rational expectations).
The management model that has run us for the past 30 years, like the discredited economic theories (rational expectations, efficient markets) to which it cringes, is bust, dead, finished - a mortal danger to us and the planet.
In fact, the debate over models has swept through economics, particularly since the crisis, and particularly when it came to the more conservative, free-market innovations over the last half century: efficient markets, rational expectations, dynamic stochastic general equilibrium models.
In Western countries, health care quality standards and expectations emphasise effective, efficient, safe, timely and equitable care in addition to person and family centred care [ 4, 6– 8].
Rational expectations and efficient markets theories are both consistent with big price crashes.
