Search Results for author:
Found 10 papers, 3 papers with code
The Bayesian Context Trees State Space Model for time series modelling and forecasting
The utility of the general framework is illustrated in two particular instances: When autoregressive (AR) models are used as base models, resulting in a nonlinear AR mixture model, and when conditional heteroscedastic (ARCH) models are used, resulting in a mixture model that offers a powerful and systematic way of modelling the well-known volatility asymmetries in financial data.
Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees
A new Bayesian modelling framework is introduced for piece-wise homogeneous variable-memory Markov chains, along with a collection of effective algorithmic tools for change-point detection and segmentation of discrete time series.
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
In addition to standard Lipschitz assumptions and conditions on the vanishing step-size sequence, it is assumed that the associated \textit{mean flow} $ \tfrac{d}{dt} \vartheta_t = \bar{f}(\vartheta_t)$, is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar{f}(\theta)=\text{ E}[f(\theta,\Phi)]$ with $\Phi$ having the stationary distribution of the chain.
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models
At the bottom level, a different real-valued time series model is associated with each context-state, i. e., with each leaf of the tree.
The Feature-First Block Model
Labelled networks are an important class of data, naturally appearing in numerous applications in science and engineering.
Population-scale testing can suppress the spread of infectious disease
Provided that results are obtained rapidly, the test frequency required to suppress an epidemic is monotonic and near-linear with respect to R0, the infectious period, and the fraction of susceptible individuals.
Bayesian Context Trees: Modelling and exact inference for discrete time series
We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series.
Methodology Information Theory Information Theory Applications Computation
Optimal rates for independence testing via $U$-statistic permutation tests
We study the problem of independence testing given independent and identically distributed pairs taking values in a $\sigma$-finite, separable measure space.
Differential Temporal Difference Learning
Value functions derived from Markov decision processes arise as a central component of algorithms as well as performance metrics in many statistics and engineering applications of machine learning techniques.
Packet Speed and Cost in Mobile Wireless Delay-Tolerant Networks
A mobile wireless delay-tolerant network (DTN) model is proposed and analyzed, in which infinitely many nodes are initially placed on R^2 according to a uniform Poisson point process (PPP) and subsequently travel, independently of each other, along trajectories comprised of line segments, changing travel direction at time instances that form a Poisson process, each time selecting a new travel direction from an arbitrary distribution; all nodes maintain constant speed.
Information Theory Information Theory
