Bayesian model
A Bayesian Model of Stress Assignment in Reading
... the model assigned the incorrect stress ...the model expressed here must incorporate an assumption that stress pattern information may be retrieved via a lexical route as well as being computed following ...
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A Bayesian Model for Learning SCFGs with Discontiguous Rules
... alignment model (Cherry and Lin, 2007; Zhang et ...the model back towards the initial alignments that they attempt to improve ...novel Bayesian model for phrasal alignment and extraction that ...
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A Bayesian Model of Diachronic Meaning Change
... namic Bayesian model of diachronic meaning change, which infers temporal word represen- tations as a set of senses and their ...explicitly model language change as a smooth, gradual pro- ...this ...
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Robust Bayesian Filtering Using Bayesian Model Averaging and Restricted Variational Bayes
... Abstract —Bayesian filters can be made robust to outliers if the solutions are developed under the assumption of heavy-tailed distributed noise. However, in the absence of outliers, these robust solutions perform ...
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Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes
... single model, when several viable models exist, limits its ...of model uncertainty, a Bayesian model averaging procedure is presented which allows for unconditional inference within the class ...
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A Bayesian Model for Joint Learning of Categories and their Features
... Previous approaches on feature learning have primarily focused on emulating or complementing norming studies by automatically extracting norm- like properties from textual corpora (e.g., elephant has-trunk, scissors ...
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Bayesian model selection for the glacial-interglacial cycle
... attempted model selection experiments for the GIG cycle, but with various limitations compared to our ...one model over any other. Feng and Bailer-Jones (2015) used Bayesian model selection to ...
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Bayesian Model Averaging in the Instrumental Variable Regression Model
... huge model space, there are many problems with conventional model selection procedures ...problems). Bayesian model averaging (BMA) can be used to avoid some of these ...the model space ...
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Bayesian model comparison with un normalised likelihood
... In this section we use SAVIS for estimating the mar- ginal likelihood for a first order Ising model on data of size 100 × 100 pixels simulated from an Ising model with parameter θ = 10. Again, estimating a ...
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A Bayesian Model for Unsupervised Semantic Parsing
... their model as a directed model with an underlying generative story specifying how the semantic structure is generated and how the syntac- tic parse is drawn for this semantic ...the model, sim- ...
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A Bayesian model for binary Markov chains
... the model was used to study the migration behavior of animal population from capture-recapture data [2]; in pathology, the model was useful to describe the evolution of certain viral or infectious diseases ...
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A Bayesian Model for Discovering Typological Implications
... into looking at which implications hold, considering only “VO” languages, or considering only languages with prepositions. It is straightforward to modify our model so that it searches over triples of features, ...
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A Bayesian model for event based trust
... beta model, where all interactions ‘weigh’ equally, and in the absence of any mathematical it is not clear what the exact benefits of this bias towards newer information ...
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A Kullback Leibler Divergence for Bayesian Model Diagnostics
... rem 2. However, our results would need further refine- ment when the normality assumptions given in (3) and (4) are not suitable (see Example 3.7). As shown in Section 4, model comparison in medical research may ...
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An Incremental Bayesian Model for Learning Syntactic Categories
... proposed model, we train it on a sample of language representative of what children would hear, and evaluate its categorization ...learning model, we also com- pare the model’s qualitative learning ...
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A Bayesian Model of Grounded Color Semantics
... corpus-based model of meaning that ac- counts for possible differences in word usage across different ...our model allows for variability in meaning by positing uncer- tainty in classification boundaries ...
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Efficient and context dependent Bayesian model selection
... The final context we consider is the situation in which models which have been built for one purpose, are ‘re-used’ as components within larger models. In applied work, this situation often arises as a result of the ...
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Multivariate Bayesian Structural Time Series Model
... MBSTS model does not show an obvious advantage in the first two plots, since the generated target time series have only a local trend or a linear ...MBSTS model beats other benchmark models in plot 3 and ...
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Asymptotic Model Selection for Naive Bayesian Networks
... for Bayesian model selection among Bayesian networks with hidden ...naive Bayesian model; it complements the main result presented by Theorem ...
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