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Cristina Adela Popescu

dblp:71/2861 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 75% Deep learning architectures and training · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
expectation-maximization		 0.112005
Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation › expectation-maximization
monte carlo expectation maximization		 0.112005
Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005
Machine learning › Deep learning architectures and training
state space model		 0.112005
Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005
Machine learning › Probabilistic and Bayesian machine learning › dynamical system
switching state-space model		 0.112005
Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005

Methods — techniques the papers use, named apart from their topics

rao-blackwellization · 0.1gibbs sampler · 0.1Monte Carlo EM · 0.1
YearPublicationVenuePosition
2005 Nested Monte Carlo EM Algorithm for Switching State-Space Models
abstract
Switching state-space models have been widely used in many applications arising from science, engineering, economic, and medical research. In this paper, we present a Monte Carlo Expectation Maximization (MCEM) algorithm for learning the parameters and classifying the states of a state-space model with a Markov switching. A stochastic implementation based on the Gibbs sampler is introduced in the expectation step of the MCEM algorithm. We study the asymptotic properties of the proposed algorithm, and we also describe how a nesting approach and the Rao-Blackwellized forms can be employed to accelerate the rate of convergence of the MCEM algorithm. Finally, the performance and the effectiveness of the proposed method are demonstrated by applications to simulated and physiological experimental data.
Cristina Adela Popescu, Yau Shu Wong
 IEEE Trans. Knowl. Data Eng.1
2004 Monte Carlo Approach for Switching State-Space Models
Cristina Adela Popescu, Yau Shu Wong
 IEA/AIE1