Search results for "estimator"
showing 10 items of 313 documents
Conditional Entropy-Based Evaluation of Information Dynamics in Physiological Systems
2014
We present a framework for quantifying the dynamics of information in coupled physiological systems based on the notion of conditional entropy (CondEn). First, we revisit some basic concepts of information dynamics, providing definitions of self entropy (SE), cross entropy (CE) and transfer entropy (TE) as measures of information storage and transfer in bivariate systems. We discuss also the generalization to multivariate systems, showing the importance of SE, CE and TE as relevant factors in the decomposition of the system predictive information. Then, we show how all these measures can be expressed in terms of CondEn, and devise accordingly a framework for their data-efficient estimation.…
Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range co…
2017
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of …
Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique
2010
We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected acc…
Entropy-Based Detection of Complexity and Nonlinearity in Short-Term Heart Period Variability under different Physiopathological States
2020
We compare different estimators of a popular en-tropy-based nonlinear dynamic measure, i.e. the conditional entropy (CE), as regards their ability to assess the complexity and nonlinearity of short-term heart rate variability (HRV). The CE is computed using binning, kernel and nearest neighbor entropy estimators in HRV time series measured from young, old and post-myocardial infarction patients studied at rest and during orthostatic stress. We find that the three estimators yield similar patterns of CE, but different patterns of nonlinear dynamics, across groups and conditions. These results suggest that the strategy for CE estimation is not crucial for the quantification of complexity, but…
Estimation of recombinant protein production in Pichia pastoris base don a constraint-based model
2012
[EN] A previously validated constraint based model and possibilistic MFA have been used to design a simple estimator of protein production rate in Pichia pastoris cultures. A structured model of the yeast P. pastoris metabolism is used to predict the balance of key energetic equivalents such as ATP from available measurements, mainly substrate consumption, gases exchange rates and biomass specific growth. It has been shown that ATP flux can be related to biomass growth and protein productivity specific rates by linear regression. Cross-validation has been applied for robust parameter fitting on the basis of chemostat, steady-state experimental conditions. In this way, protein estimation can…
Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology
2008
Analyzing spatiotemporal dependencies between different types of events is highly relevant to many biological phenomena (e.g., signaling and trafficking), especially as advances in probes and microscopy have facilitated the imaging of dynamic processes in living cells. For many types of events, the segmented areas can overlap spatially and temporally, forming random clumps. In this paper, we model the binary image sequences of two different event types as a realization of a bivariate temporal random set and propose a nonparametric approach to quantify spatial and spatiotemporal interrelations using the pair correlation, cross-covariance, and the Ripley K functions. Based on these summary st…
Integrating LSTMs with Online Density Estimation for the Probabilistic Forecast of Energy Consumption
2019
In machine learning applications in the energy sector, it is often necessary to have both highly accurate predictions and information about the probabilities of certain scenarios to occur. We address this challenge by integrating and combining long short-term memory networks (LSTMs) and online density estimation into a real-time data streaming architecture of an energy trader. The online density estimation is done in the MiDEO framework, which estimates joint densities of data streams based on ensembles of chains of Hoeffding trees. One attractive feature of the solution is that queries can be sent to the here-called forecast-based point density estimators (FPDE) to derive information from …
Forest of Normalized Trees: Fast and Accurate Density Estimation of Streaming Data
2018
Density estimation of streaming data is a relevant task in numerous domains. In this paper, a novel non-parametric density estimator called FRONT (forest of normalized trees) is introduced. It uses a structure of multiple normalized trees, segments the feature space of the data stream through a periodically updated linear transformation and is able to adapt to ever evolving data streams. FRONT provides accurate density estimation and performs favorably compared to existing online density estimators in terms of the average log score on multiple standard data sets. Its low complexity, linear runtime as well as constant memory usage, makes FRONT by design suitable for large data streams. Final…
A ML Estimator of the Correlation Dimension for Left-hand Truncated Data Samples
2002
— A maximum-likelihood (ML) estimator of the correlation dimension d 2 of fractal sets of points not affected by the left-hand truncation of their inter-distances is defined. Such truncation might produce significant biases of the ML estimates of d 2 when the observed scale range of the phenomenon is very narrow, as often occurs in seismological studies. A second very simple algorithm based on the determination of the first two moments of the inter-distances distribution (SOM) is also proposed, itself not biased by the left-hand truncation effect. The asymptotic variance of the ML estimates is given. Statistical tests carried out on data samples with different sizes extracted from populatio…
On new efficient algorithms for PIMC and PIMD
2002
Abstract The properties of various algorithms, estimators, and high-temperature density matrix (HTDM) decompositions relevant for path integral simulations are discussed. It is shown that Fourier accelerated path integral molecular dynamics (PIMD) completely eliminates slowing down with increasing Trotter number P . A new primitive estimator of the kinetic energy for use in PIMD simulations is found to behave less pathologically than the original virial estimator. In particular, its variance does not increase significantly with P . Two non-primitive HTDM decompositions are compared as well: one decomposition used in the Takahashi Imada algorithm and another one based on an effective propaga…