Bayesian Estimation of Inequality and Poverty Indices in Case of Pareto Distribution Using Different Priors under LINEX Loss Function

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Text-based language identification for the South African languages

Subjects: “…Naïve bayesian classification…”
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Calibrating a stochastic SIR-model to simulated data using different calibration methods : a tutorial & comparison of methods by Van Staden, Wynand-Junior

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Modelling the South African tuberculosis epidemic: the effect of HIV, sex differences, and the impact of interventions by Kubjane, Mmamapudi

“…These factors are distributed differently by sex and likely explain the observed sex disparities in TB. …”
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Bayesian parameter estimation for process monitoring by Basson, Marno

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Subjective Bayesian analysis of elliptical models

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Bayesian machine learning : theory and applications by Payne, Megan Wendy

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Bayesian signal processing of doppler radar data by De Villiers, Charl Felix

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Applying dynamic Bayesian Networks to process monitoring by Wakefield, Brandon Jason

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Training Feedforward Neural Networks with Bayesian Hyper-Heuristics

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The use of bayesian neural networks in thyroid cancer classification by Du Preez, Tiana

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Data modelling & Bayesian model comparison with spherically symmetric priors by Jamodien, Riyaadh

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A semantic Bayesian network for automated share evaluation on the JSE by Drake, Rachel

“…This will alleviate the need for manual analysis and reduce the subjective nature of investment decision making. However, there are different investment approaches and perspectives for investing which makes acquiring and representing expert knowledge for share evaluation challenging. …”
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Bayesian approaches of Markov models embedded in unbalanced panel data by Muller, Christoffel Joseph Brand

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Regional CO₂ flux estimates for South Africa through inverse modelling by Nickless, Alecia

“…Bayesian inverse modelling provides a top-down technique of verifying emissions and uptake of carbon dioxide (CO₂) from both natural and anthropogenic sources. …”
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Volatility Model Pricing and Calibration with Neural Networks using Bayesian Optimisation by Goosen, Jenna

“…Implied volatilities for these models are accurately estimated using a numerical integration approach, Monte Carlo approach, series expansion approximation or using a two factor finite difference approach. However, these volatility calculations are computationally expensive. …”
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Predicting financial distress of JSE-Listed companies using Bayesian networks by Cassim, Ziyad

“…Various permutations of Bayesian models are tested relating to different learning algorithms, intervals of discretisation and scoring metrics. …”
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Empirical statistical modelling for crop yields predictions: bayesian and uncertainty approaches by Adeyemi, Rasheed Alani

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