In this paper we investigate whether a combination of topic specific language models can outperform a general purpose language model, using a trigram model as our baseline model. We show that in the ideal case — in which it is known beforehand which model to use — specific models perform considerably better than the baseline model. We test two methods that combine specific models and show that ...

All mathematical models of real-world phenomena contain parameters that need to be estimated from measurements, either for realistic predictions or simply to understand the characteristics of the model. Bayesian statistics provides a framework for parameter estimation in which uncertainties about models and measurements are translated into uncertainties in estimates of parameters. This paper pr...

here in, an application of a new seismic inversion algorithm in one of iran’s oilfields is described. stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. this method integrates information from different data sources with different scales, as prior informat...

This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averag...

The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortunately, the original inference algorithm is rather complicated, and takes time, where is the length of the sequence, making it impractical for many domains. In this paper, we show how HHMMs are a special kind of dynamic ...