Bayesian inference of COVID-19 spreading rates in South Africa
The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has
highlighted the need for performing accurate inference with limited data. Fundamental to the …
highlighted the need for performing accurate inference with limited data. Fundamental to the …
[HTML][HTML] Separable shadow Hamiltonian hybrid Monte Carlo for Bayesian neural network inference in wind speed forecasting
Accurate wind speed and consequently wind power forecasts form a critical enabling tool for
large scale wind energy adoption. Probabilistic machine learning models such as Bayesian …
large scale wind energy adoption. Probabilistic machine learning models such as Bayesian …
Stock price prediction using sentiment analysis
We investigate the influence of financial news headline sentiment on the predictability of
stock prices using Long Term Short Term Memory (LSTM) networks. The investigation is …
stock prices using Long Term Short Term Memory (LSTM) networks. The investigation is …
Bayesian inference of local government audit outcomes
The scandals in publicly listed companies have highlighted the large losses that can result
from financial statement fraud and weak corporate governance. Machine learning …
from financial statement fraud and weak corporate governance. Machine learning …
Antithetic magnetic and shadow hamiltonian monte carlo
Hamiltonian Monte Carlo is a Markov Chain Monte Carlo method that has been widely
applied to numerous posterior inference problems within the machine learning literature …
applied to numerous posterior inference problems within the machine learning literature …
On data-driven management of the COVID-19 outbreak in South Africa
The rapid spread of the novel coronavirus (SARS-CoV-2) has highlighted the need for the
development of rapid mitigating responses under conditions of extreme uncertainty. While …
development of rapid mitigating responses under conditions of extreme uncertainty. While …
[图书][B] Hamiltonian Monte Carlo methods in machine learning
Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal
tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC …
tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC …
Probabilistic inference of south african equity option prices under jump-diffusion processes
Jump-diffusion processes have been utilised to capture the leptokurtic nature of asset
returns and to fit the market observed option volatility skew with great success. These …
returns and to fit the market observed option volatility skew with great success. These …
[图书][B] Hybrid Monte Carlo methods in machine learning: stochastic volatility methods, shadow Hamiltonians, adaptive approaches and variance reduction techniques
WT Mongwe - 2022 - search.proquest.com
Abstract Markov Chain Monte Carlo (MCMC) methods are a vital inference tool for
probabilistic machine learning models. A commonly utilised MCMC algorithm is the …
probabilistic machine learning models. A commonly utilised MCMC algorithm is the …
Parameter Inference Using Probabilistic Techniques
R Mbuvha - 2021 - search.proquest.com
Complex non-linear prediction systems have become ubiquitous in numerous decision
making and other socio-technical systems. In recent years, the increased adoption and use …
making and other socio-technical systems. In recent years, the increased adoption and use …