Posterior Likelihood for the HPYP Topic Model
A unified posterior regularized topic model with maximum margin for learning-to-rank
... LTR model, which is known to perform well on this task [22, 14, 1, 7], with information derived from a latent topic model, which has already proven beneficial in many IR tasks [30, 32, ...existing ...
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Evaluating Topic Quality with Posterior Variability
... Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model ...
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Likelihood free model choice
... each model under ...the posterior distribution by a member of an exponential family, using an iterative and fast moment-matching process that takes only a component of the likelihood product at a ...
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Likelihood-free model choice
... each model under ...the posterior distribution by a member of an exponential family, using an iterative and fast moment-matching process that takes only a component of the likelihood product at a ...
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HTM: A Topic Model for Hypertexts
... of topic distributions from cited documents, the words of a document can be more accurately as- signed into ...largest posterior probabilities for the words are also ...
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VODUM: a Topic Model Unifying Viewpoint, Topic and Opinion Discovery
... a Topic Model ...per-word likelihood. As comput- ing the perplexity of a Topic Model is intractable, an estimate of the perplexity is usually computed using the parameters’ point ...
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Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks
... total likelihood to be zero as ...true model already predicts some ...the model so that it encompasses more than one response ...diffusion model to account for contaminations of ...
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A Revised Inference for Correlated Topic Model
... variational posterior parameters so that they are kept close to their initial values inherited from CGS for ...some posterior parameters close to their values initialized based on a result of CGS for ...
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An Entity Topic Model for Entity Linking
... they model the context compatibility and the topic coherence separately, which makes it difficult to capture the mutual reinforcement effect between the above two ...the topic coherence and the ...
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A Topic Model for Word Sense Disambiguation
... perform posterior inference, which is the task of determining the conditional distribution of the hidden variables given the ...the topic assign- ments of each word in the collection, and the synset path of ...
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Dynamic joint sentiment-topic model
... dJST model is motivated by two ...JST model assumes that words in text have a static co-occurrence pat- tern, which may not be suitable for the task of capturing topic and sentiment shifts in a ...
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Probit Normal Correlated Topic Model
... correlated topic modeling structure based on the traditional multi- nomial probit and then tested the computational speed for key sampling ...high posterior dependency structure between auxiliary variables ...
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CompareLDA: A Topic Model for Document Comparison
... a topic model supervised by pairwise compar- isons of ...a model seeks to yield topics that help to differentiate entities along some dimension of inter- est, which may vary from one application to ...
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Structure of persuasive communication and elaboration likelihood model
... a topic under discussion, the attack is not explicit and specifically directed either, it rather aims at “overall” undermining the person, showing him in a negative light and not necessarily in the context of the ...
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Bayesian model comparison with un normalised likelihood
... marginal likelihood is done in three stages: firstly b θ is estimated; followed by Z (b θ), then finally the marginal ...the posterior expectation, estimated from a run of the exchange algorithm of 10, 000 ...
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Online topic model for Twitter considering dynamics of user interests and topic trends
... 3.2 Model Inference We use a stochastic expectation-maximization al- gorithm for Twitter-TTM inference, as described in Wallach (2006) in which Gibbs sampling of la- tent values and maximum joint likelihood ...
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Topic Segmentation with a Structured Topic Model
... the model fully nonparametric and investigate the effects of adding different cues in texts, such as cue phrases, pronoun usage, prosody, ...our model uses marginal boundary probabilities to generate the ...
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Efficacy of topic ocular hipotensive agents after posterior capsulotomy
... Descritores: Hipertensão ocular; Cápsula do cristalino; Lasers; Anti-hipertensivos Objetivo: Analisar e comparar os efeitos dos agentes hipotensores tópi- cos, sobre a pressão intra-ocular (PIO), após capsulotomia ...
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Multiple Testing under Dependence with Approximate Posterior Likelihood and Related Topics
... the posterior probabilities of binary signals at individual sites of the process, by drawing strength from observations at nearby sites without assuming the availability of their joint prior ...
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Bilingual Segmented Topic Model
... Table 5 shows the average number of seg- ments per article for each model. As can be seen, BiSTM+TS divides an article into segments smaller than the original sections. This seems to be reasonable, because some ...
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