Polya Urn Models

By Hosam M. Mahmoud. 2008

453 citations recorded by the source corpus.

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In Mathematical Physics

Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, background): “When β = 1, only popularity choices are allowed, yielding a type of Polya urn model [22].”
Optimizing Semantic Coherence in Topic Models (2011, unknown): “The process for generating a sequence of words from such a model is known as the simple P´olya urn model (Mahmoud, 2008), in which the initial probability of word type w in topic t is proportional to β , while the probability of each subsequent occurrence of w in topic t is proportional to the…”
Limit distributions for large P\'{o}lya urns (2009, methodology): “ed so that citing all contributions has become hopeless. The following references give however a good idea of the variety of methods: combinatorics with many papers by H. Mahmoud (see his recent book [15]), probabilistic methods by means of embedding the process in continuous time (see Janson [9]), ”
Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, methodology): “2 Shown in Figure 1, the proposed GPU-DMM extends the Dirichlet Multinomial Mixture (DMM) model by incorporating the learned word relatedness from large text corpus through the generalized P ´ o lya urn (GPU) model [18] in topic inferences.”
Mining topics in documents: standing on the shoulders of big data (2014, background): “The generalized Pólya urn (GPU) model [22, 24] differs from SPU in that, when a ball of a certain color is drawn, two balls of that color are put back along with a certain number of balls of some other colors.”
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, background): “Pólya urn model [29] is a type of statistical model with selfreinforcing property, sometimes referred as “the rich get richer”.”
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (2014, methodology): “…KNOWLEDGE AND DEALING WITH WRONG KNOWLEDGE As each pk-set reflects a possible semantic similarity relation between a pair of words, we use the generalized Pólya urn (GPU) model (Mahmoud, 2008) to leverage this knowledge in Gibbs sampling to encourage the pair of words to appear in the same topic.”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, methodology): “Based on DMM model, GPU-DMM [29] promotes the semantically related words under the same topic during the sampling process by the generalized Pólya urn (GPU) model [41].”