Bayesian Time Series Models

By David Barber, Ali Taylan Cemgil, Silvia Chiappa. 2011

113 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Effective Bayesian Modeling of Groups of Related Count Time Series (2014, background): “…spirit of hierarchical models studied in statistics (Gelman & Hill, 2007) and machine learning (Teh & Jordan, 2010; Fox et al., 2010), we let those parameters (for all time series ` ∈ {1, . . . , L} that belong to the group being modeled simultaneously) share common parents, as illustrated on Fig.”
MAVEN: Multi-Agent Variational Exploration (2019, background): “where the nonnegativity of the KL divergence on the righthand side implies that a bad variational approximation can hurt performance as it induces a gap between the true objective and the lower bound [32, 2].”
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling (2018, background): “Bayesian state space models and inference algorithms enable posterior inference of the latent states of a system and the parameters that govern their dynamics (Särkkä, 2013; Barber et al., 2011; Doucet et al., 2001).”
Exact post-selection inference for the generalized lasso path (2018, background): “Instead of attempting to thoroughly review the changepoint detection literature, we refer the reader to the comprehensive surveys and reviews in Brodsky & Darkhovski (1993), Chen & Gupta (2000), Eckley et al. (2011). Broadly speaking, a changepoint detection problem is one in which the distribution ”
Monte Carlo methods (2013, methodology): “To tackle large dimensions, other methods have been devised that are based on a non-independent sampling: Markov chain Monte Carlo methods (MCMC).”
Deep Factors with Gaussian Processes for Forecasting (2018, methodology): “Classical time series methods, such as Autoregressive Integrated Moving Average (ARIMA) [3], exponential smoothing [4] and general Bayesian time series [5], excel at modeling the complex dynamics of individual time series of sufficiently long history.”
Bayesian Intermittent Demand Forecasting for Large Inventories (2016, methodology): “We employ approximate Bayesian inference in a linear dynamical system, for which there is a lot of prior work in Machine Learning [3, 1, 2].”
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale (2017, methodology): “We employ approximate Bayesian inference in a linear dynamical system, for which there is a lot of prior work in Machine Learning [3, 1, 2].”