Finite Mixture and Markov Switching Models

By Sylvia Frühwirth-Schnatter. 2006

1,872 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/43b329f879b16c17d8699c755f4ecc5d40408904

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Citations in the corpus

K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation? (2015, methodology): “The way we proceed is to adjust the multi-move sampler described in Chib (1996) to the time-varying Markov structure in the transition probabilities, see e.g. Frühwirth-Schnatter (2006), Algorithm 11.1 and 11.2.”
Stochastic variational inference for hidden Markov models (2014, methodology): “Typically, for HMMs a structured mean field approximation is considered: q(θ,x) = q(A)q(φ)q(x), (4) breaking dependencies only between the parameters θ = {A, φ} and latent state sequence x [17].”
Evaluating Probabilistic Forecasts with scoringRules (2017, methodology): “The model is estimated using Bayesian Markov chain Monte Carlo methods (Frühwirth-Schnatter 2006, e.g.,).”
Bayesian semiparametric stochastic volatility modeling (2010, background): “For a more detailed discussion of this in the context of finite mixture models see Geweke (2007) and Frühwirth-Schnatter (2006).”
GARCH Models (2010, neutral): “For switching regimes models, two recent references are the monographs by Cappé, Moulines and Rydén (2005), and by Frühwirth-Schnatter (2006).”
An Adaptive Sequential Monte Carlo Sampler (2010, methodology): “The ability of the ASMC algorithm to learn MCMC tuning parameters in more complicated scenarios is now evaluated using simulated data from a range of mixture likelihoods (for a complete review of this topic, see Frühwirth-Schnatter (2006)).”
Time‐Varying Transition Probabilities for Markov Regime Switching Models (2017, methodology): “We refer to the textbook of Frühwirth-Schnatter (2006) for an extensive introduction and discussion.”
Model-based clustering of categorical time series (2010, methodology): “As in Frühwirth-Schnatter and Kaufmann (2008), we use an MCMC sampler described in Algorithm 1, see also the short note by Ridgeway and Altschuler (1998):”
Mixtures of Experts Models (2018, background): “McLachlan & Peel (2000) and Frühwirth-Schnatter (2006) provide background to a range of mixtures of experts models; Masoudnia & Ebrahimpour (2014) survey the ME literature from a machine learning perspective.”