Matches in SemOpenAlex for { <https://semopenalex.org/work/W853443624> ?p ?o ?g. }
- W853443624 abstract "This thesis presents theoretical studies of some stochastic processes and their appli- cations in the Bayesian nonparametric methods. The stochastic processes discussed in the thesis are mainly the ones with independent increments - the Levy processes. We develop new representations for the Levy measures of two representative exam- ples of the Levy processes, the beta and gamma processes. These representations are manifested in terms of an infinite sum of well-behaved (proper) beta and gamma dis- tributions, with the truncation and posterior analyses provided. The decompositions provide new insights into the beta and gamma processes (and their generalizations), and we demonstrate how the proposed representation unifies some properties of the two, as these are of increasing importance in machine learning. Next a new Levy process is proposed for an uncountable collection of covariate- dependent feature-learning measures; the process is called the kernel beta process. Available covariates are handled efficiently via the kernel construction, with covari- ates assumed observed with each data sample (customer), and latent covariates learned for each feature (dish). The dependencies among the data are represented with the covariate-parameterized kernel function. The beta process is recovered as a limiting case of the kernel beta process. An efficient Gibbs sampler is developed for computations, and state-of-the-art results are presented for image processing and music analysis tasks. Last is a non-Levy process example of the multiplicative gamma process applied in the low-rank representation of tensors. The multiplicative gamma process is applied along the super-diagonal of tensors in the rank decomposition, with its shrinkage property nonparametrically learns the rank from the multiway data. This model is constructed as conjugate for the continuous multiway data case. For the non- conjugate binary multiway data, the Polya-Gamma auxiliary variable is sampled to elicit closed-form Gibbs sampling updates. This rank decomposition of tensors driven by the multiplicative gamma process yields state-of-art performance on various synthetic and benchmark real-world datasets, with desirable model scalability." @default.
- W853443624 created "2016-06-24" @default.
- W853443624 creator A5078791969 @default.
- W853443624 date "2014-01-01" @default.
- W853443624 modified "2023-09-26" @default.
- W853443624 title "Application of Stochastic Processes in Nonparametric Bayes" @default.
- W853443624 cites W1500188831 @default.
- W853443624 cites W1517266559 @default.
- W853443624 cites W152832198 @default.
- W853443624 cites W1529851927 @default.
- W853443624 cites W1545357565 @default.
- W853443624 cites W1591798773 @default.
- W853443624 cites W1638985343 @default.
- W853443624 cites W1746819321 @default.
- W853443624 cites W1783767608 @default.
- W853443624 cites W1884516485 @default.
- W853443624 cites W1965614296 @default.
- W853443624 cites W1968552545 @default.
- W853443624 cites W1969508365 @default.
- W853443624 cites W1987532879 @default.
- W853443624 cites W2004026774 @default.
- W853443624 cites W2024165284 @default.
- W853443624 cites W2029721016 @default.
- W853443624 cites W2040006565 @default.
- W853443624 cites W2048987737 @default.
- W853443624 cites W2053218206 @default.
- W853443624 cites W2054323527 @default.
- W853443624 cites W2054864979 @default.
- W853443624 cites W2056099894 @default.
- W853443624 cites W205829674 @default.
- W853443624 cites W2069429561 @default.
- W853443624 cites W2081321853 @default.
- W853443624 cites W2082600181 @default.
- W853443624 cites W2099878672 @default.
- W853443624 cites W2101802482 @default.
- W853443624 cites W2104827998 @default.
- W853443624 cites W2105767795 @default.
- W853443624 cites W2106221905 @default.
- W853443624 cites W2109614047 @default.
- W853443624 cites W2114727905 @default.
- W853443624 cites W2117111086 @default.
- W853443624 cites W2117234597 @default.
- W853443624 cites W2126658427 @default.
- W853443624 cites W2128002512 @default.
- W853443624 cites W2128378502 @default.
- W853443624 cites W2132267493 @default.
- W853443624 cites W2134032957 @default.
- W853443624 cites W2138689104 @default.
- W853443624 cites W2140433132 @default.
- W853443624 cites W2147512299 @default.
- W853443624 cites W2150286230 @default.
- W853443624 cites W2151792436 @default.
- W853443624 cites W2154099718 @default.
- W853443624 cites W2158266063 @default.
- W853443624 cites W2158535911 @default.
- W853443624 cites W2160047866 @default.
- W853443624 cites W2160407462 @default.
- W853443624 cites W2204613168 @default.
- W853443624 cites W2263034332 @default.
- W853443624 cites W2607405526 @default.
- W853443624 cites W2797641211 @default.
- W853443624 cites W2802739963 @default.
- W853443624 cites W2962942617 @default.
- W853443624 cites W2964042789 @default.
- W853443624 cites W3106384737 @default.
- W853443624 cites W41179353 @default.
- W853443624 cites W68132019 @default.
- W853443624 cites W2958652704 @default.
- W853443624 cites W3145738572 @default.
- W853443624 hasPublicationYear "2014" @default.
- W853443624 type Work @default.
- W853443624 sameAs 853443624 @default.
- W853443624 citedByCount "0" @default.
- W853443624 crossrefType "dissertation" @default.
- W853443624 hasAuthorship W853443624A5078791969 @default.
- W853443624 hasConcept C102366305 @default.
- W853443624 hasConcept C105795698 @default.
- W853443624 hasConcept C107673813 @default.
- W853443624 hasConcept C118615104 @default.
- W853443624 hasConcept C119043178 @default.
- W853443624 hasConcept C119857082 @default.
- W853443624 hasConcept C149782125 @default.
- W853443624 hasConcept C154945302 @default.
- W853443624 hasConcept C17744445 @default.
- W853443624 hasConcept C199539241 @default.
- W853443624 hasConcept C2776359362 @default.
- W853443624 hasConcept C28826006 @default.
- W853443624 hasConcept C33923547 @default.
- W853443624 hasConcept C41008148 @default.
- W853443624 hasConcept C74193536 @default.
- W853443624 hasConcept C79495835 @default.
- W853443624 hasConcept C8272713 @default.
- W853443624 hasConcept C88757350 @default.
- W853443624 hasConcept C94625758 @default.
- W853443624 hasConceptScore W853443624C102366305 @default.
- W853443624 hasConceptScore W853443624C105795698 @default.
- W853443624 hasConceptScore W853443624C107673813 @default.
- W853443624 hasConceptScore W853443624C118615104 @default.
- W853443624 hasConceptScore W853443624C119043178 @default.
- W853443624 hasConceptScore W853443624C119857082 @default.