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Learning Vector Autoregressive Models With Latent Processes

AAAI 2018 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Latent variable marginalization and estimators · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Estimate observed-process dynamics using past observations beyond the current time stepVAR model with latent processes · Synthetic DRG(0.4,0.4)support estimation errorHelped
Increase the observed-to-latent noise power ratio (OLNR)VAR linear-measurement estimation · Synthetic DRG(0.05,0.05)average estimation errorHelped
Use the proposed linear-measurement estimation and recovery procedureVAR model with one latent macroeconomic process, GPDI · US macroeconomic data, 1-Jun-2009 to 31-Dec-2016network recoveryHelped
Use the proposed linear-measurement estimation and recovery procedureVAR model with butter prices latent · Dairy prices, January 1986 to December 2016network recoveryHelped
Use the proposed linear-measurement estimation and recovery procedureVAR model with X3 latent · West German macroeconomic data, 1960-1982network recoveryHelped
Increase the number of latent variablesDTR recovery assumption on random graphs · Synthetic DRG(p,1/n), n=100probability of satisfying the assumptionHurt
Increase edge probability p when p is relatively largeDTR recovery assumption on random graphs · Synthetic DRG(p,1/n), n=100probability of satisfying the assumptionHurt
Use the NM algorithm under the stated path-uniqueness conditionsMinimum-latent-network recovery · Synthetic DRG(1/2n,1/2n)instances recovering all possible minimal networksHelped
Select observed variables in the high-power regimeObserved-subnetwork support estimation · US macroeconomic data, 31-Mar-1947 to 31-Mar-2009support estimation errorHelped
Use the proposed linear-measurement approach when genericity assumptions of prior work do not holdVAR model with butter prices latent · Dairy prices, January 1986 to December 2016network recoveryHelped

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