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Predictive Statistical Modelling Approaches for Events in Discrete Space and Time

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dc.contributor.advisor Sinha, Ankur
dc.contributor.author SAHU, SHUBHANKAR
dc.date.accessioned 2024-05-16T08:51:29Z
dc.date.available 2024-05-16T08:51:29Z
dc.date.issued 2024-05
dc.identifier.citation 72 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/8798
dc.description.abstract This thesis investigates the use of predictive modelling to improve decision-making for events that are distributed over discrete space and occur in discrete time. We explore three main statistical methods: Kernel Density Estimation (KDE), Bayesian methods, and Maximum Likelihood Estimation (MLE) known for their effectiveness in analyzing data and applicability to continuous and discrete time scenarios. A key contribution is developing an advanced KDE model that features adaptive bandwidth and weighted approaches for more accurate density estimates. We focus our modelling approaches on coal production data from various mines; we demonstrate how these predictive models can address uncertainties and periodic trends in mine output. Accurately forecasting mine production can enhance resource allocation and inventory management. The thesis further extends to prescriptive analytics by incorporating mathematical optimization to allocate coal optimally from mines to power plants. This involves formulating and solving a mathematical model with real-world constraints to find the best allocation solution. By combining predictive and prescriptive analytics, these research findings have the potential to significantly impact the sectors where future events can occur only in discrete space and time, leading to more informed and data-driven decisions. en_US
dc.language.iso en en_US
dc.subject Research Subject Categories::MATHEMATICS en_US
dc.title Predictive Statistical Modelling Approaches for Events in Discrete Space and Time en_US
dc.type Thesis en_US
dc.description.embargo Two Years en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Mathematics en_US
dc.contributor.registration 20191090 en_US


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  • MS THESES [1713]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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