  The COVID-19 pandemic has had a long-term impact on industries worldwide,
with the hospitality and food industry facing significant challenges, leading
to the permanent closure of many restaurants and the loss of jobs. In this
study, we developed an innovative analytical framework using Hamiltonian Monte
Carlo for predictive modeling with Bayesian regression, aiming to estimate the
change point in consumer behavior towards different types of restaurants due to
COVID-19. Our approach emphasizes a novel method in computational analysis,
providing insights into customer behavior changes before and after the
pandemic. This research contributes to understanding the effects of COVID-19 on
the restaurant industry and is valuable for restaurant owners and policymakers.
