139 citations · 364 across the 12 of their papers we have counts for
13 papers
Nonasymptotic Gaussian Approximation for Inference with Stable Noise
Marina Riabiz, Tohid Ardeshiri, Ioannis Kontoyiannis +1
The results of a series of theoretical studies are reported, examining the convergence rate for different approximate representations of -stable distributions. Although they pla…
Robust Bayesian Filtering and Smoothing Using Student's t Distribution
Michael Roth, Tohid Ardeshiri, Emre Özkan +1
State estimation in heavy-tailed process and measurement noise is an important challenge that must be addressed in, e.g., tracking scenarios with agile targets and outlier-corrupte…
Approximate Recursive Identification of Autoregressive Systems with Skewed Innovations
Henri Nurminen, Tohid Ardeshiri
We propose a novel recursive system identification algorithm for linear autoregressive systems with skewed innovations. The algorithm is based on the variational Bayes approximatio…
Identification of Piecewise Affine State-Space Models via Expectation Maximization
Rafael Rui, Tohid Ardeshiri, Alexandre Bazanella
This paper deals with the identification of piecewise affine state-space models. These models are obtained by partitioning the state or input domain into a finite number of regions…
State Estimation for Piecewise Affine State-Space Models
Rafael Rui, Tohid Ardeshiri, Henri Nurminen +2
We propose a filter for piecewise affine state-space (PWASS) models. In each filtering recursion, the true filtering posterior distribution is a mixture of truncated normal distrib…
Skew-t Filter and Smoother with Improved Covariance Matrix Approximation
Henri Nurminen, Tohid Ardeshiri, Robert Piché +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based…
Skew-t inference with improved covariance matrix approximation
Henri Nurminen, Tohid Ardeshiri, Robert Piche +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t distributed measurement noise are presented. The proposed algorithms improve upon our ear…
Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family
Tohid Ardeshiri, Umut Orguner, Fredrik Gustafsson
In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised f…
Gaussian Mixture Reduction Using Reverse Kullback-Leibler Divergence
Tohid Ardeshiri, Umut Orguner, Emre Özkan
We propose a greedy mixture reduction algorithm which is capable of pruning mixture components as well as merging them based on the Kullback-Leibler divergence (KLD). The algorithm…
Maximum entropy properties of discrete-time first-order stable spline kernel
Tianshi Chen, Tohid Ardeshiri, Francesca P. Carli +3
The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a ze…
Robust Inference for State-Space Models with Skewed Measurement Noise
Henri Nurminen, Tohid Ardeshiri, Robert Piché +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes…
Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
Tohid Ardeshiri, Emre Özkan, Umut Orguner +1
We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique t…
Maximum Entropy Property of Discrete-time Stable Spline Kernel
Tohid Ardeshiri, Tianshi Chen
In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain inste…