The research paper shows a few different applications of them for classification and regression problems. The probabilistic graphical mode would be given be the Bayesian Network. BNNs are comprised of a probabilistic model and a Neural Network. It indicates the stock price returning and their conditional independencies through a cyclic graph extending standard networks with posterior inference in order to control over fitting. Bayesian Neural Networks for stock prices forecasting before and   COVID -19 pandemic during Markov Chain Monte Carlo’s (MCMC) sampling methods have been standing out in implementing inference of Bayesian Neural Network, many parameters and the need for better computational resources. The COVID-19 pandemic had a drastic impact in the world economy and stock markets given different levels of lockdowns due to rise and fall of daily infections. It is important to investigate the performance of related forecasting models during the COVID-19 pandemic given the volatility in stock markets. In this paper, we use novel Bayesian neural networks for multi-step-ahead stock price forecasting.

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Keywords: MCMC (Markov Chain Monte Carlo).