Abstract
In this chapter, you’ll carry out a set of sequential methods to discern patterns in confirmed COVID-19 cases in the US. To begin, you’ll use the Gaussian Hidden Markov Model to inherit the series, model it, and consider the hidden states, including the means and covariance in those states. Subsequently, you’ll use the Monte Carlo simulation method to replicate confirmed US COVID-19 cases across multiple trials, therefore providing a rich comprehension of patterns in the data.
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Nokeri, T.C. (2022). A Case for COVID-19: Considering the Hidden States and Simulation Results. In: Artificial Intelligence in Medical Sciences and Psychology. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-8217-5_3
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DOI: https://doi.org/10.1007/978-1-4842-8217-5_3
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-8216-8
Online ISBN: 978-1-4842-8217-5
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