Search Results - "MOULINES, E."

CONVERGENCE OF ADAPTIVE AND INTERACTING MARKOV CHAIN MONTE CARLO ALGORITHMS by Fort, G., Moulines, E., Priouret, P.

“…Adaptive and interacting Markov chain Monte Carlo algorithms (MCMC) have been recently introduced in the literature. These novel simulation algorithms are…”
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Variance reduction for Markov chains with application to MCMC by Belomestny, D., Iosipoi, L., Moulines, E., Naumov, A., Samsonov, S.

“…In this paper, we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic…”
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Blocking strategies and stability of particle Gibbs samplers by SINGH, S. S., LINDSTEN, F., MOULINES, E.

“…Sampling from the posterior probability distribution of the latent states of a hidden Markov model is nontrivial even in the context of Markov chain Monte…”
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Fast incremental expectation maximization for finite-sum optimization: nonasymptotic convergence by Fort, G., Gach, P., Moulines, E.

“…Fast incremental expectation maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental…”
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Variance reduction for additive functionals of Markov chains via martingale representations by Belomestny, D., Moulines, E., Samsonov, S.

“…In this paper, we propose an efficient variance reduction approach for additive functionals of Markov chains relying on a novel discrete-time martingale…”
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Diffusion Approximations and Control Variates for MCMC by Brosse, N., Durmus, A., Meyn, S., Moulines, E., Samsonov, S.

“…A new method is introduced for the construction of control variates to reduce the variance of additive functionals of Markov Chain Monte Carlo (MCMC) samplers…”
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A blind source separation technique using second-order statistics by Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., Moulines, E.

“…Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori…”
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A Wavelet Whittle Estimator of the Memory Parameter of a Nonstationary Gaussian Time Series by Moulines, E., Roueff, F., Taqqu, M. S.

“…We consider a time series $X=\{X_{k},k\in {\Bbb Z}\}$ with memory parameter $d_{0}\in {\Bbb R}$. This time series is either stationary or can be made…”
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Subspace methods for the blind identification of multichannel FIR filters by Moulines, E., Duhamel, P., Cardoso, J.-F., Mayrargue, S.

“…This paper addresses a problem arising in a context of digital communications. A digital source is transmitted through a continuous channel (the propagation…”
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A central limit theorem for adaptive and interacting Markov chains by FORT, G., MOULINES, E., PRIOURET, P., VANDEKERKHOVE, P.

“…Adaptive and interacting Markov Chains Monte Carlo (MCMC) algorithms are a novel class of non-Markovian algorithms aimed at improving the simulation efficiency…”
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A subspace algorithm for certain blind identification problems by Abed-Meraim, K., Loubaton, P., Moulines, E.

“…The problem of blind identification of p-inputs/q-outputs FIR transfer functions is addressed. Existing subspace identification methods derived for p=1 are…”
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Error Exponents for Neyman-Pearson Detection of a Continuous-Time Gaussian Markov Process From Regular or Irregular Samples by Hachem, W, Moulines, E, Roueff, F

“…This paper addresses the detection of a stochastic process in noise from a finite sample under various sampling schemes. We consider two hypotheses. The noise…”
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ASYMPTOTIC PROPERTIES OF U-PROCESSES UNDER LONG-RANGE DEPENDENCE by Lévy-Leduc, C, Boistard, H, Moulines, E, Taqqu, M. S., Reisen, V. A.

“…Let (X i ) i≥1 be a stationary mean-zero Gaussian process with covariances ρ(k) = 𝔼(X 1 X k+1 ) satisfying ρ(0) = 1 and ρ(k) = k− D L(k), where D is in (0,…”
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On the Spectral Density of the Wavelet Coefficients of Long-Memory Time Series with Application to the Log-Regression Estimation of the Memory Parameter by Moulines, E., Roueff, F., Taqqu, M. S.

“…  In recent years, methods to estimate the memory parameter using wavelet analysis have gained popularity in many areas of science. Despite its widespread…”
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Prediction error method for second-order blind identification by Abed-Meraim, K., Moulines, E., Loubaton, P.

“…Blind channel identification methods based on the oversampled channel output are a problem of current theoretical and practical interest. In this paper, we…”
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On Approximate Maximum-Likelihood Methods for Blind Identification: How to Cope With the Curse of Dimensionality by Barembruch, S., Garivier, A., Moulines, E.

“…We discuss approximate maximum-likelihood methods for blind identification and deconvolution. These algorithms are based on particle approximation versions of…”
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On Recursive Estimation for Time Varying Autoregressive Processes by Moulines, Eric, Priouret, Pierre, Roueff, François

“…This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and…”
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Forgetting the initial distribution for Hidden Markov Models by Douc, R., Fort, G., Moulines, E., Priouret, P.

“…The forgetting of the initial distribution for discrete Hidden Markov Models (HMM) is addressed: a new set of conditions is proposed, to establish the…”
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Central limit theorem for the robust log-regression wavelet estimation of the memory parameter in the Gaussian semi-parametric context by KOUAMO, O., LÉVY-LEDUC, C., MOULINES, E.

“…In this paper, we study robust estimators of the memory parameter d of a (possibly) non-stationary Gaussian time series with generalized spectral density f…”
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Quantitative Bounds on Convergence of Time-Inhomogeneous Markov Chains by R. Douc, Moulines, E., Rosenthal, Jeffrey S.

“…Convergence rates of Markov chains have been widely studied in recent years. In particular, quantitative bounds on convergence rates have been studied in…”
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