to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture

We use the link between financial constraints and a firm’s demand for liquidity to develop a new test of the effect of financial constraints on firm policies. The effect of financial constraints can be captured by a firm’s propensity to save cash out of incremental cash inflows (the cash flow

In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures', of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative

Present day systems, intelligent or otherwise, are limited by the conceptualizations of the world given to them by their designers. In this paper, we propose a novel, first-principles approach to performing incremental reformulations for computational efficiency. First, we define a reformulation

Abstract: The article discusses definitions of vector space for variance increment and standard deviation increment, as well as def-inition of scalar product for variance increment. This justifies using a vector calculus for variance increment and allows for emplyoing vector calculus methods

antecedents of rules have the status of default conditions, and they are the only ones that may be left unproved, so only plausible candidate plans are produced. These are refined incrementally by trying to justify the assumptions on which they depend. The new planning strategy has been implemented, with good

In answer set programming, the existence of an answer set for a logic program is not guaranteed. In order to remedy this problem, we utilize the alternative concept of ι-answer sets, which are characterized by their applied rules. The ι-answer sets of a logic program amount to the justified

: batch-incremental methods that gather examples in batches to train models; and instance-incremental methods that learn from each example as it arrives. Typically, papers in the literature choose one of these approaches, but provide insufficient evidence or references to justify their choice. We provide

for elicitation of probabilities, relatively little work has addressed the elicitation of utility models. This imbalance is not particularly justified considering that probability models are relatively stable across problem instances, while utility models may be different for each instance. Spending large amounts

an object followed by grasping, lifting and holding of that object in a physically-realistic three-dimensional environment. The method assumes that success is dependent on the order in which behaviors are learned. This is justified by results which show that if a robot is forced to learn lifting first