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We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic re-lationships in the model make it effectively impossible to use the EM algori...
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
A single, stationary topic model such as la-tent Dirichlet allocation is inappropriate for modeling corpora that span long time peri-ods, as the popularity of topics is likely to change over time. A n...
Recognizing group activities is challenging due to the di-culties in isolating individual entities, nding the respective roles played by the individuals and representing the complex interactions amo...
The Buridan's ass paradox is characterized by perpetual indecision between two states, which are never attained. When this problem is formulated as a dynamical system, indecision is modeled by a disc...
We are grateful to the discussants for their posi-tive and interesting comments. In an area moving so rapidly it is to be expected that our review overlooks some work, and all the contributions helpf...
We congratulate the authors for producing such a helpful and comprehensive overview paper of a ra-pidly developing and important area. The starting point for inference in spatial extreme value prob-le...
The review paper on spatial extremes by Davison,Padoan and Ribatet is a most welcome contribu-tion. The authors cover quite a lot of ground, mak-ing connections between different approaches while high...
We congratulate the authors for their overview paper discussing modeling techniques for spatial ex-tremes. There is great interest in spatial extreme data in the atmospheric science community, as the...
The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood ...
In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and ...
Nonresponse is present in almost all surveys and can severely bias estimates. It is usually distinguished between unit and item nonresponse: in the former, we completely fail to have information from ...
In the mixture modeling frame, this paper presents the polynomial Gaussian cluster-weighted model (CWM). It extends the linear Gaussian CWM, for bivariate data, in a twofold way. Firstly, it allows fo...
The vector autoregressive (VAR) model has been widely used for modeling temporal de-pendence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can ...

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