According to the Hick's law, reaction times increase linearly with the uncertainty of target stimuli. We tested the generality of this law by measuring reaction times in a human sequence learning protocol involving serial target locations which differed in transition probability and global entropy. Our results showed that sigmoid functions better describe the relationship between reaction times...

In this work, we introduce several models for grapheme-tophoneme conversion: a conditional maximum entropy model, a joint maximum entropy n-gram model, and a joint maximum entropy n-gram model with syllabification. We examine the relative merits of conditional and joint models for this task, and find that joint models have many advantages. We show that the performance of our best model, the joi...

Structural economic models allow one to analyze counter-factuals when economic system change and to evaluate the well being of economic agents. A key element in such analysis is the ability to identify the primitive functions and distributions of the economic models that are employed to describe the economic phenomena under study. Recent developments in nonparametric identification of structura...

The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. The result carries over to models for duration and realised volatility that use an exponential link function. A key feature of the model formulation is that the dynamics are driven by the score. Keywords: Duration models; g...

in this study, the relationship between inflation and economic growth of iran is conciliated with a perspective on uncertainty of inflation. we use a generalized autoregressive conditional heteroskedasticity (garch) model that make possible this advantage that conditional variance of error term changes along the time, also, vector error correction model (vecm) is stable. for surveying the co-in...

We present a method for conditional maximum likelihood estimation of N-gram models used for text or speech utterance classification. The method employs a well known technique relying on a generalization of the Baum-Eagon inequality from polynomials to rational functions. The best performance is achieved for the 1-gram classifier where conditional maximum likelihood training reduces the class er...

In this paper, we propose an efficient method for drawing shape samples using a point distribution model (PDM) that is conditioned on given points. This technique is suited for sample-based segmentation methods that rely on a PDM, e.g. [6], [2] and [3]. It enables these algorithms to effectively constrain the solution space by considering a small number of user inputs – often one or two landmar...