nils-herrmann/clean_scirepeval huggingface dataset:
Space alternating data augmentation: application to finite mixture of Gaussians and speaker recognition

The SAGE (space-alternating generalized expectation-maximization) algorithm (Celeux, G. et al., 2001) is one of the most elegant and popular extensions of the EM (expectation maximization) algorithm for performing ML (maximum likelihood) or MAP (maximum a posteriori) parameter estimation. This algorithm updates parameter components by subblocks by alternating missing data spaces. Its efficiency has been reported in numerous simulation studies. We propose here an MCMC (Markov chain Monte Carlo) strategy named SADA (space-alternating data augmentation) which relies on the same principle in order to sample efficiently from (posterior) distributions and we discuss its application to finite mixtures of Gaussians. For this model, we also present an original implementation of the SAGE algorithm. In Monte Carlo simulations and in an application for speaker recognition, these methods, which are straightforward modifications of the standard EM and DA (data augmentation) algorithms, consistently outperform them.
Joint target tracking and identification. Part II. Shape video computing

For Pt. I see ibid., vol.1 p.256-299, (2005). This paper describes an application of sequential Monte Carlo model-based approaches to perform joint target tracking and identification. While a geometric shape is moving inside the field of view of a CCD camera, alternatively getting closer and moving away while rotating, the data processing system is confronted to challenging tasks: track the moving shape in real 3D space, i.e. estimate its position and orientation, and at the same time dynamically estimate its dimensions and, if required, identify it. The system is based on class-specific Bayesian filters. More originally, the issue of the fixed hyper-parameter estimation, here the geometric shape dimensions, is solved by combining two different techniques. The first one consists of Markov chain Monte Carlo moves that rescale both the trajectory and the shape; it benefits from an efficient statistic which summaries the trajectory with regard to moves. The second one is an artificial deformation diffusion of the shape.
A Rao-Blackwellized particle filter for INS/GPS integration

The localization performance of a navigation system can be improved by coupling different types of sensors. The paper focuses on INS-GPS integration. INS and GPS measurements allow a non-linear state space model, which is appropriate to particle filtering, to be defined. This model being conditionally linear Gaussian, a Rao-Blackwellization procedure can be applied to reduce the variance of the estimates.
A new FA property based genetic algorithm for improving source and channel estimates

We introduce a new genetic algorithm, which exploits the finite alphabet (FA) property to refine the estimates of information source symbols and channel estimates obtained by any identification algorithm. The new genetic source symbol refinement algorithm (GSR) is tested to cope with rapidly time-varying finite impulse response (FIR) channels with additive noise model. Computational results show that, as compared with recent sophisticated alternatives for the problem, it offers superior performance with reduced complexity.
A simulation based algorithm for optimal quantization in non-linear/non-Gaussian state-space models

The problem of the approximation of the optimal filter for non-linear/non-Gaussian state-space models is considered in this paper. This problem is studied for models with a multi-dimensional (continuous) state space and one-dimensional (continuous) observation space. An approximation of the optimal filter based on quantization is proposed. We quantize both the state and observation processes to obtain a hidden Markov model with discrete state and observation spaces for which the optimal filter can be computed exactly. The problem of the optimal selection of the parameters of this approximating model (quantization thresholds, states, transition probabilities, likelihood probabilities) is considered. An algorithm based on Monte Carlo gradient estimation and stochastic approximation is proposed. The asymptotic properties of the proposed algorithm are analyzed and sufficient conditions for its convergence are also obtained.
Particle Motions in Absorbing Medium with Hard and Soft Obstacles

We consider a particle evolving according to a Markov motion in an absorbing medium. We analyze the long term behavior of the time at which the particle is killed and the distribution of the particle conditional upon survival. Under given regularity conditions, these quantities are characterized by the limiting distribution and the Lyapunov exponent of a nonlinear Feynman-Kac operator. We propose to approximate numerically this distribution and this exponent based on various interacting particle system interpretations of the Feynman-Kac operator. We study the properties of the resulting estimates.
Particle Filtering for Joint Symbol and Code Delay Estimation in DS Spread Spectrum Systems in Multipath Environment

We develop a new receiver for joint symbol, channel characteristics, and code delay estimation for DS spread spectrum systems under conditions of multipath fading. The approach is based on particle filtering techniques and combines sequential importance sampling, a selection scheme, and a variance reduction technique. Several algorithms involving both deterministic and randomized schemes are considered and an extensive simulation study is carried out in order to demonstrate the performance of the proposed methods.
Monte Carlo Smoothing for Nonlinear Time Series

We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filtered trajectories.
Nonlinear filtering approaches for INS/GPS integration

Navigation with an integrated INS/GPS approach requires to solve a set of nonlinear equations. In this case, nonlinear filtering techniques such as Particle Filtering methods are expected to perform better than the classical, but suboptimal, Extended Kalman Filter. Besides, the INS/GPS model has a conditionally linear Gaussian structure. A Rao-Blackwellization procedure can then be applied to reduce the variance of the state estimates. This paper studies different algorithms combining Rao-Blackwellization and particle filtering for a specific INS/GPS scenario. Simulation results illustrate the performance of these algorithms. The variance of the estimates is also compared to the corresponding posterior Cramer-Rao bound.
Probability hypothesis density filter versus multiple hypothesis tracking

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates only the first order moment instead of the full multi-target posterior. Recently, a sequential Monte Carlo (SMC) implementation of PHD filter has been used in multi-target filtering with promising results. In this paper, we will compare the performance of the PHD filter with that of the multiple hypothesis tracking (MHT) that has been widely used in multi-target filtering over the past decades. The Wasserstein distance is used as a measure of the multi-target miss distance in these comparisons. Furthermore, since the PHD filter does not produce target tracks, for comparison purposes, we investigated ways of integrating the data-association functionality into the PHD filter. This has lead us to devise methods for integrating the PHD filter and the MHT filter for target tracking which exploits the advantage of both approaches.
The cross-entropy method for blind multiuser detection

We consider the problem of blind multiuser detection. We adopt a Bayesian approach where unknown parameters are considered random and integrated out. Computing the maximum a posteriori estimate of the input data sequence requires solving a combinatorial optimization problem. We propose here to apply the Cross-Entropy method recently introduced by Rubinstein. The performance of cross-entropy is compared to Markov chain Monte Carlo. For similar Bit Error Rate performance, we demonstrate that Cross-Entropy outperforms a generic Markov chain Monte Carlo method in terms of operation time.
Fixed-lag sequential Monte Carlo

Sequential Monte Carlo methods, aka particle methods, are an efficient class of simulation techniques to approximate sequences of complex probability distributions. These probability distributions are approximated by a large number of random samples called particles which are propagated over time using a combination of importance sampling and resampling steps. The efficiency of these algorithms is highly dependent on the importance distribution used. Even if the optimal importance distribution is chosen, the algorithm can be inefficient. Indeed, current standard sampling strategies extend the paths of particles over one time step and weight them consistently but do not modify the locations of the past of the paths. Consequently, if the discrepancy between two successive probability distributions is high, then this strategy can be highly inefficient. In this paper, we propose an extended importance sampling technique that allows us to modify the past of the paths and weight them consistently without having to perform any local Monte Carlo integration. This approach reduces the depletion of samples. An application to an optimal filtering problem for a toy nonlinear state space model illustrates this methodology.
Particle filtering for multi-target tracking using jump Markov systems

In multi-target tracking, one jointly estimates the number of targets and the individual target states from sensor measurements. This is a challenging problem due to the time varying number of targets and unknown measurement to target associations. We present a particle filtering method for multi-target tracking. The proposed method applies particle filtering techniques to a jump Markov system that models the multi-target dynamics. Simulation results using this particle method are also presented.
Two time-scale stochastic approximation for constrained stochastic optimization and constrained Markov decision problems

In this paper, constrained stochastic optimization problems are considered for the case where the constraint functions are convex (but the criterion function can be non-convex) and the criterion and constraint functions are available only through their noisy observations. A general algorithm of the two time-scale stochastic approximation type is proposed for these problems. The proposed algorithm is applied to Markov decision problems with average cost, average constraints and parameterized stationary policy. The asymptotic behavior of the proposed algorithm is analyzed for the case where the algorithm step-sizes are constant and the noise in the observations of the criterion and constraint functions depends on the algorithm iterates.
RADIAL BASIS FUNCTION REGRESSION USING TRANS-DIMENSIONAL SEQUENTIAL

We consider the general problem of sampling from a sequence of distributions that is defined on a union of subspaces. We will illustrate the general approach on the problem of sequential radial basis function (RBF) regression where the number of kernels is variable and unknown. Our approach, which we term Trans-Dimensional Sequential Monte Carlo (TD-SMC), is based on a generalisation of importance sampling to spaces of variable dimension. In the spirit of  we augment the target parameter space at the current time step with an auxiliary space corresponding to the parameters at the previous time step. This facilitates the design of efficient proposal distributions, which can then be formulated as moves from the auxiliary parameter space to the target parameter space, lending our algorithm its sequential character. These proposals are very general, and may include within model moves to update parameters, and trans-dimensional birth or death moves to add or remove parameters when appropriate. From this perspective our approach is reminiscent of the Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) algorithm .
Random finite sets and sequential Monte Carlo methods in multi-target tracking

The random finite set provides a rigorous foundation for optimal Bayes multi-target filtering. The major hurdle faced in Bayes multi-target filtering is the inherent computational intractability. Even the probability hypothesis density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multi-target posterior, still involves multiple integrals with no closed forms. In this paper, we highlight the relationship between the Radon-Nikodym derivative and the set derivative of random finite sets that enables a sequential Monte Carlo (SMC) implementation of the optimal multitarget filter. In addition, a generalised SMC method to implement the PHD filter is also presented. The SMC PHD filter has an attractive feature - its computational complexity is independent of the (time-varying) number of targets.
Maintaining multimodality through mixture tracking

In recent years particle filters have become a tremendously popular tool to perform tracking for nonlinear and/or nonGaussian models. This is due to their simplicity, generality and success over a wide range of challenging applications. Particle filters, and Monte Carlo methods in general, are however poor at consistently maintaining the multimodality of the target distributions that may arise due to ambiguity or the presence of multiple objects. To address this shortcoming this paper proposes to model the target distribution as a nonparametric mixture model, and presents the general tracking recursion in this case. It is shown how a Monte Carlo implementation of the general recursion leads to a mixture of particle filters that interact only in the computation of the mixture weights, thus leading to an efficient numerical algorithm, where all the results pertaining to standard particle filters apply. The ability of the new method to maintain posterior multimodality is illustrated on a synthetic example and a real world tracking problem involving the tracking of football players in a video sequence.
Radial basis function regression using trans-dimensional sequential Monte Carlo

We consider the general problem of sampling from a sequence of distributions that is defined on a union of sub-spaces. We will illustrate the general approach on the problem of sequential radial basis function (RBF) regression where the number of kernels is variable and unknown. Our approach, which we term trans-dimensional sequential Monte Carlo (TD-SMC), is based on a generalisation of importance sampling to spaces of variable dimension. In the spirit of P. Del Moral and A. Doucet (2002) we augment the target parameter space at the current time step with an auxiliary space corresponding to the parameters at the previous time step. This facilitates the design of efficient proposal distributions, which can then be formulated as moves from the auxiliary parameter space to the target parameter space, lending our algorithm its sequential character. These proposals are very general, and may include within model moves to update parameters, and trans-dimensional birth or death moves to add or remove parameters when appropriate. From this perspective our approach is reminiscent of the reversible jump Markov Chain Monte Carlo (RJ-MCMC) algorithm [ 1995].
A new class of soft MIMO demodulation algorithms

We propose a new class of soft-input soft-output demodulation schemes for multiple-input multiple-output (MIMO) channels, based on the sequential Monte Carlo (SMC) framework under both stochastic and deterministic settings. The stochastic SMC sampler generates MIMO symbol samples based on importance sampling and resampling techniques, whereas the deterministic SMC approach recursively performs exploration and selection steps in a greedy manner. By exploiting the artificial sequential structure of the existing simple Bell-Labs layered space-time (BLAST) detection method based on nulling and cancellation, the proposed algorithms achieve an error probability performance that is orders of magnitude better than the traditional BLAST detection schemes while maintaining a low computational complexity. In fact, the new methods offer performance comparable with that of the sphere decoding algorithm without attendant increase in complexity. More importantly, being soft-input soft-output in nature, both the stochastic and deterministic SMC detectors can be employed as the first-stage demodulator in a turbo receiver in coded MIMO systems. Such a turbo receiver successively improves the receiver performance by iteratively exchanging the so-called extrinsic information between the soft outer channel decoder and the inner soft MIMO demodulator under both known channel state and unknown channel state scenarios. Computer simulation results are provided to demonstrate the performance of the proposed algorithms.
Blind multi-user detectors exploiting spatial diversity in DS-CDMA downlink

We propose second-order-statistics (SOS) based spatial diversity exploiting the subspace methods to blindly identify the downlink channel of direct sequence-code division multiple access (DS-CDMA) systems. Once the channel has been identified, a linear receiver is used to recover the transmitted symbols. The new techniques supports 100% loading. It is shown that base on the identified signal subspace and the channel parameters, linear detectors can be constructed in closed form at a reduced computational cost.
Particle filtering for joint symbol and parameter estimation in DS spread spectrum systems

In this paper, we develop a new receiver for joint symbol, channel characteristics and code delay estimation for DS spread spectrum systems under conditions of multipath fading. This nonlinear estimation problem is extremely complex. An efficient simulation-based algorithm based on particle filtering is proposed to solve it. The method combines sequential importance sampling, a selection scheme and a variance reduction technique. An extensive simulation study is carried out and demonstrates good performance of the suggested approach.
Stochastic approximation for optimal observer trajectory planning

A maneuvering target is to be tracked based on noise corrupted measurements of the target's state that are received by a moving observer. Additionally, the quality of the target state observations can be improved by the appropriate positioning of the observer relative to the target during tracking. The bearings-only tracking problem is an example of this scenario. The question of optimal observer trajectory planning naturally arises, i.e. how should the observer maneuver relative to the target in order to optimise the tracking performance? In this paper, we formulate this problem as a discrete-time stochastic optimal control problem and present a novel stochastic approximation algorithm for designing the observer trajectory. Numerical examples are presented to demonstrate the utility of the proposed methodology.
A New Class of Soft MIMO

We propose a new class of soft-input soft-output demodulation schemes for multiple-input multiple-output (MIMO) channels, based on the sequential Monte Carlo (SMC) framework under both stochastic and deterministic settings. The stochastic SMC sampler generates MIMO symbol samples based on importance sampling and resampling techniques, whereas the deterministic SMC approach recursively performs exploration and selection steps in a greedy manner. By exploiting the artificial sequential structure of the existing simple Bell-Labs layered space-time (BLAST) detection method based on nulling and cancellation, the proposed algorithms achieve an error proba- bility performance that is orders of magnitude better than the traditional BLAST detection schemes while maintaining a low computational complexity. In fact, the new methods offer perfor- mance comparable with that of the sphere decoding algorithm without attendant increase in complexity. More importantly, being soft-input soft-output in nature, both the stochastic and deterministic SMC detectors can be employed as the first-stage demodulator in a turbo receiver in coded MIMO systems. Such a turbo receiver successively improves the receiver performance by iteratively exchanging the so-called extrinsic information between the soft outer channel decoder and the inner soft MIMO demodulator under both known channel state and unknown channel state scenarios. Computer simulation results are provided to demonstrate the performance of the proposed algorithms.
Sequential Bayesian Kernel Regression

We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm overcomes some of the computational difficulties related to batch methods for kernel regression. It is non-iterative, and requires only a single pass over the data. It is thus applicable to truly sequential data sets and batch data sets alike. The algorithm is based on a generalisation of Importance Sampling, which allows the design of intuitively simple and efficient proposal distributions for the model parameters. Comparative results on two standard data sets show our algorithm to compare favourably with existing batch estimation strategies.
Parameter estimation in general state-space models using particle methods

Particle filtering techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. If the model includes fixed parameters, a standard technique to perform parameter estimation consists of extending the state with the parameter to transform the problem into an optimal filtering problem. However, this approach requires the use of special particle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and gradient algorithms to perform batch and recursive maximum likelihood parameter estimation. An original particle method is presented to implement these approaches and their efficiency is assessed through simulation.
Sampling-based near-optimal MIMO demodulation algorithms

We propose a new class of soft-input soft-output demodulation schemes for multiple-input multiple-output (MIMO) channels, based on the sequential Monte Carlo (SMC) framework under both stochastic and deterministic settings. By exploiting the artificial sequential structure of the existing simple BLAST detection method based on nulling and cancellation, the proposed algorithms achieve an error probability performance that is orders of magnitude better than the traditional BLAST detection schemes while maintaining a low computational complexity. In fact, the new methods offer performance comparable with that of the sphere decoding algorithm, without attendant increase in complexity. Computer simulation results are provided to demonstrate the performance of the proposed algorithms.