This paper introduces a new multinomial approach unifying the computation of confidence intervals for Markovian models. Starting from a method used for homogeneous Markov chains, we show that it can be applied on models incorporating a hidden component. We consider three models derived from the basic homogeneous Markov chain: the Mixture Transition Distribution (MTD) model, the Hidden Markov Mo...

In this paper we consider the nite sample properties of least squares system identi cation, and we derive non-asymptotic con dence ellipsoids for the estimate. Unlike asymptotic theory, the obtained con dence ellipsoids are valid for a nite number of data points. The probability that the estimate belongs to a certain ellipsoid has a natural dependence on the volume of the ellipsoid, the data ge...

In comparing two treatments with failure time observations, con®dence bands for the `̀ difference'' of two survival curves provide useful information about a global picture of the treatment difference over time. In this note, we propose a rather simple procedure for constructing such simultaneous con®dence intervals. Our technique can also be used in the one-sample case, which has been extensive...

This paper proposes an adaptive Mahalanobis distance for face retrieval. The distance is derived from a posterior distribution of observation errors in features categorized by con dence of face images. Since the distance is calculated considering error variances of each dimension according to the con dence, it can re ect error distribution of each matching more precisely than a standard Mahalan...

We characterize preference relations over bounded below Anscombe and Aumann’s acts and give necessary and su¢ cient conditions that guarantee the existence of a utility function u on consequences, a con…dence function ' on the set of all probabilities over states of nature and a positive threshold level of con…dence 0 such that our preference relation has a functional representation J , where g...

A modi ed decoding algorithm for automatic speech recognition (ASR) will be described which facilitates a closer coupling between the acoustic and language modeling components of a speech recognition system. This closer coupling is obtained by extracting word level measures of acoustic con dence during decoding, and making coded representations of these con dence measures available to the ASR n...

An important motivation for software testing is to increase con dence that the software no longer contains faults. In this paper we explain a technique for using fewer tests to gain an equivalent con dence in software. Our techniques complement random black box testing. To be able to use fewer tests and gain equivalent con dence, we must either (1) isolate and remove software characteristics th...

Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable con dence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid con dence measures to predictions of classical machine learning algorithms. ...

Within the machine translation system Verbmobil, translation is performed simultaneously by four independent translation modules. The four competing translations are combined by a selection module so as to form a single optimal output for each input utterance. The selection module relies on con dence values that are delivered together with each of the alternative translations. Since the con den...

Within Valiant's model of learning as formalized by Kearns, we show that computable total predicates for two formally uncomputable problems (the classical Halting Problem, and the Halting Problem relative to a speci ed oracle) are formally learnable from examples, to arbitrarily high accuracy with arbitrarily high con dence, under any probability distribution. The Halting Problem relative to th...