A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New nonlinear smoothers improve state estimation in chaotic systems.
We present a Kalman smoothing framework based on modeling errors using the heavy tailed Student's t distribution, along with algorithms, convergence theory, open-source general implementation, and several important applications. The computational effort per iteration grows linearly with the length of the time series, a…
Unified framework for ensemble transport-based smoothing of non-Gaussian time series.
Unified framework for efficient Gaussian process inference.
EnKBS smoothes complex systems with future observations for causal inference.
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and …
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering. The most well-known method, the Kalman smoother (and the related Kalman filter), relies on assumptions of linearity and Gaussianity that are rarely met in practice. In this p…
Kalman filtering and smoothing algorithms are used in many areas, including tracking and navigation, medical applications, and financial trend filtering. One of the basic assumptions required to apply the Kalman smoothing framework is that error covariance matrices are known and given. In this paper, we study a general…
Data assimilation for subsurface flow using latent diffusion models shows that ensemble Kalman methods may overestimate posterior uncertainty, while Monte Carlo sampling is more reliable.
The paper stabilizes PD term structures under forecast uncertainty using a Kalman filter with an anchored observation model.
Introduces Gaussian Processes and Relevance Vector Machines, connecting them to Kalman filtering.
Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.
TASC improves synthetic control for time-series data with trends.
Develops a novel ML smoothing method for incomplete data in state-space models.
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coeffic…
Parallel-in-time solver reduces ODE simulation time from linear to logarithmic.
We analyzed the performance of a biologically inspired algorithm called the Corrected Projections Algorithm (CPA) when a sparseness constraint is required to unambiguously reconstruct an observed signal using atoms from an overcomplete dictionary. By changing the geometry of the estimation problem, CPA gives an analyti…
We introduce a class of quadratic support (QS) functions, many of which play a crucial role in a variety of applications, including machine learning, robust statistical inference, sparsity promotion, and Kalman smoothing. Well known examples include the l2, Huber, l1 and Vapnik losses. We build on a dual representation…
New method combines ODE solvers with Bayesian inference for efficient model training.
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists ac…
State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch-Tung-Striebel and Mayne-Fraser algorithms. Such schemes are equivalent to linear algebraic techniques that minimize a conv…
Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the Gaussian process prior used in LFMs can be equivalently formulated…
We begin by reiterating that common neural network activation functions have simple Bayesian origins. In this spirit, we go on to show that Bayes's theorem also implies a simple recurrence relation; this leads to a Bayesian recurrent unit with a prescribed feedback formulation. We show that introduction of a context in…
Forest-guided smoothing uses random forest outputs for interpretable local smoothers.
This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the Boosting algorithm and provides a new statistical interpretation for Boosting. We analyze the behavior of the Boosting algorithm applied to commo…
Improved Kalman filter for Stiefel manifold measurements.
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
Reliable 4D aircraft trajectory prediction, whether in a real-time setting or for analysis of counterfactuals, is important to the efficiency of the aviation system. Toward this end, we first propose a highly generalizable efficient tree-based matching algorithm to construct image-like feature maps from high-fidelity m…
Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.
The article improves prediction by aggregating Kalman recursions online.
dSMC improves parallel processing of state-space models.
Paper analyzes ensemble Kalman updates for effective dimension and localization.
In this paper, we revisit the Kalman filter theory. After giving the intuition on a simplified financial markets example, we revisit the maths underlying it. We then show that Kalman filter can be presented in a very different fashion using graphical models. This enables us to establish the connection between Kalman fi…
Illustrates interleaved learning with Kalman Filter for linear least squares.
Graph Kalman filters adapt classical filters to graph data.
Combining deep learning and ensemble smoothers for better history matching.
Recursive KalmanNet combines neural networks with Kalman filters for precise state estimation.
Paper introduces EnDKF for more accurate pose tracking.
Proposes a new method to enhance neural learning by maximizing information gain.
The extended Kalman filter is perhaps the most standard tool to estimate in real time the state of a dynamical system from noisy measurements of some function of the system, with extensive practical applications (such as position tracking via GPS). While the plain Kalman filter for linear systems is well-understood, th…
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model. The model requires a linear relationship between states and observations. Extensions to the Kalman filter have been proposed that incorporate linear approximations to nonlinear m…
This paper proves long-time accuracy of ensemble Kalman filters for chaotic and machine-learned systems.
A Kalman filter reduces valuation risk in business valuation models.
Transformers can approximate Kalman Filtering in linear systems with small error.
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems wi…
Two proofs of Kalman Theorem using flows of vector fields.
Study uses Kalman-Filter to assess market efficiency in major stock markets.