Comparing a process of laborand capital-augmenting technical change directed by capitalists’ maximization of profits with a counterfactual in which decentralized innovation decisions are governed by noncapitalist property relations, I claim that if the two economies start from the same technology and capital stock there’s a date T such that after T per-capita consumption is always strictly grea...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...

This paper introduces two constraints on the use of variables inside the expectation formula. The constraints are, roughly, (1) that the variable takes the same conditional expectation in each event in the partition and (2) that the total expected value be a linear function of the variable for each event in the partition. We argue that the reasoning in one version of the “two envelope paradox” ...

In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly-labeled data. GE fits model parameters by favoring models that match certain expectation constraints, such as marginal label distributions, on the unlabeled data. This paper shows how to apply generalized expectation criteria to two classes ...

Estimators of the conditional expectation, i.e., prediction, function involve a global bias-variance trade off. In some cases, an estimator that yields unbiased estimates of the conditional expectation for a particular partitioning of the data may be desirable. Such estimators are calibrated with respect to the partitioning. We identify the conditional expectation given a particular partitionin...

This paper shows how to compute a second-order accurate solution of a non-linear rational expectation model using algorithms developed for the solution of linear rational expectation models. The result is a state-space representation for the realized values of the variables of the model. This state-space representation can easily be used to compute impulse responses as well as conditional and u...

We consider the Braching Random Walk of height n and show that the conditional expectation of a gaussian variable at a typical vertex, under positivity, is at least a factor of log n away from the expected maxima.

The expectation maximization (EM) algorithm computes maximum likelihood estimates of unknown parameters in probabilistic models involving latent variables. More pragmatically speaking, the EM algorithm is an iterative method that alternates between computing a conditional expectation and solving a maximization problem, hence the name expectation maximization. We will in this work derive the EM ...