Kalman filter mixture model for spike sorting of non-stationary data.

اسناد مشابه

• Spike Sorting: Bayesian Clustering of Non-Stationary Data

  سند علمی۲۰۰۴Journal of neuroscience methodsAharon Bar-Hillel, Adam Spiro, Eran Stark

  Spike sorting involves clustering spikes recorded by a micro-electrode according to the source neurons. It is a complicated task, which requires much human labor, in part due to the non-stationary nature of the data. We propose to automate the clustering process in a Bayesian fra

• A Robust Non-linear Multivariate Kalman Filter for Arbitrage Identification in High Frequency Data

  سند علمی۱۹۹۶P. J. BOLLAND

  We present a methodology for modelling real world high frequency financial data. The methodology copes with the erratic arrival of data and is robust to additive outliers in the data set. Arbitrage pricing relationships are formulated into a linear state space representation. Arb

• Exploratory Modelling of Multiple Non-stationary Time Series: Latent Process Structure and Decompositions the Eeg Data and Context Arose from Discussions with Dr

  سند علمی۱۹۹۷Raquel Prado

  We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with uni-variate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent fac

• Adaptive regularization for Lasso models in the context of non-stationary data streams

  سند علمی۲۰۱۶Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana

  Large scale, streaming datasets are ubiquitous in modern machine learning. Streaming algorithms must be scalable, amenable to incremental training and robust to the presence of non-stationarity. In this work consider the problem of learning `1 regularized linear models in the con

• Metabomxtr: an R package for mixture-model analysis of non-targeted metabolomics data

  سند علمی۲۰۱۴BioinformaticsMichael Nodzenski, Michael J. Muehlbauer, James R. Bain, Anna C. Reisetter

  SUMMARY Non-targeted metabolomics technologies often yield data in which abundance for any given metabolite is observed and quantified for some samples and reported as missing for other samples. Apparent missingness can be due to true absence of the metabolite in the sample or pr

• Data Computational Modelling of Multivariable Non-Stationary Noisy Linear Systems by MOESP_AOKI_VAR Algorithm

  سند علمی۲۰۱۵Johanna B. Tobar, Celso P. Bottura, Celso Bottura

  The main objective of this work is to develop a recursive algorithm for identification in the state-space of linear stochastic discrete multivariable non-stationary system; a computational process called MOESP_AOKI_VAR is proposed and implemented to achieve this. The proposed alg