Efficient Bayesian Inference for Dynamic Mixture Models

By Richard Gerlach, Chris Carter, Robert Kohn. 2000

166 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d1379cdf69151ee99a348302c1bb2a07ad938d8d

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Citations in the corpus

Time Varying Dimension Models (2012, unknown): “The dynamic mixture model of Gerlach, Carter, and Kohn (2000) adds to (1) the assumption that any or all of the system matrices, Zt , Qt , and Ht , depend on an s × 1 vector Kt .”
Bayesian Analysis of the Heterogeneity Model (2004, background): “Furthermore from the result reported in Gerlach et al. (2000) we expect that it is also desirable to sample the group indicators from the density where the random effects are integrated out.”
Let's take a break: Trends and cycles in US real GDP (2009, methodology): “It is important to note that, using Lemma 1 of Gerlach et al. (2000), the parameters α 1 , σ 2 γ1 and σ 2 γ2 are not identified, though the trend-cycle decomposition is.”
Real-Time Inflation Forecasting in a Changing World (2009, unknown): “Our Gibbs sampler is a combination of the Kuo and Mallick (1998) algorithm for variable selection and the efficient sampling algorithm of Gerlach et al. (2000) to handle the (occasional) structural breaks.”
Time-varying sparsity in dynamic regression models (2014, unknown): “This leads to a dynamic mixture model for which efficient posterior computational methods can be developed using the approach of Gerlach et al. (2000).”
Dynamic Detection of Change Points in Long Time Series (2007, background): “While many variants exist and may be more efficient (see, for instance, Pitt and Shephard, 1999), the algorithm above, initially proposed by Gordon et al. (1993), typically works well for filtering a state space model whose hidden Markov process mixes well.”
Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, unknown): “…and the error covariance matrix can change over time.1 The change in parameters overtime is modeled based on the mixture innovation method of Gerlach et al. (2000) and Giordani and Kohn (2008), that is regarded as a special Bayesian stochastic search approach to selecting restrictions for…”
Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models (2008, methodology): “That is, on average, the adaptive sampling scheme reaches a given Monte Carlo standard error in about 29% of the time required by algorithm 1 in Gerlach et al. (2000).”