8,663 research outputs found
Latent Markov model for longitudinal binary data: An application to the performance evaluation of nursing homes
Performance evaluation of nursing homes is usually accomplished by the
repeated administration of questionnaires aimed at measuring the health status
of the patients during their period of residence in the nursing home. We
illustrate how a latent Markov model with covariates may effectively be used
for the analysis of data collected in this way. This model relies on a not
directly observable Markov process, whose states represent different levels of
the health status. For the maximum likelihood estimation of the model we apply
an EM algorithm implemented by means of certain recursions taken from the
literature on hidden Markov chains. Of particular interest is the estimation of
the effect of each nursing home on the probability of transition between the
latent states. We show how the estimates of these effects may be used to
construct a set of scores which allows us to rank these facilities in terms of
their efficacy in taking care of the health conditions of their patients. The
method is used within an application based on data concerning a set of nursing
homes located in the Region of Umbria, Italy, which were followed for the
period 2003--2005.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS230 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Tutorial on Bayesian Nonparametric Models
A key problem in statistical modeling is model selection, how to choose a
model at an appropriate level of complexity. This problem appears in many
settings, most prominently in choosing the number ofclusters in mixture models
or the number of factors in factor analysis. In this tutorial we describe
Bayesian nonparametric methods, a class of methods that side-steps this issue
by allowing the data to determine the complexity of the model. This tutorial is
a high-level introduction to Bayesian nonparametric methods and contains
several examples of their application.Comment: 28 pages, 8 figure
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
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