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There are numerous forecasting methods, and these approaches take only data, analyse it, produce a prediction by analysing, ignore the prior information side, and do not take into account the variations that occur over time. The Bayesian structural time series (BSTS) models are the best way to forecast CO2emissions and is updated. Because CO2emissions play an essential part in climate change, forecasting future CO2emissions is critical for all countries where global warming is a hazard to the planet. This study models and forecasts CO2emissions in Afghanistan from 1990 to 2019 using the BSTS models, bsts function from the bsts R package statistical tool. We did a diagnostics test of the normality of the residuals out of the bsts R package. According to the findings for 12 years ahead, CO2emissions will rise by 2031 in all models findings. The study’s findings indicate that CO2emissions in Afghanistan are projected to rise, exposing the country to climate-related concerns.

In this paper, an endeavor has been made to fit three distributions Marshall–Olkin with exponential distributions, Marshall–Olkin with exponentiated exponential distributions and Marshall–Olkin with exponentiated extension distribution keeping in mind the end goal to actualize Bayesian techniques to examine visualization of prognosis of women with breast cancer and demonstrate through utilizing Stan. Stan is an abnormal model dialect for Bayesian displaying and deduction. This model applies to a genuine survival controlled information with the goal that every one of the ideas and calculations will be around similar information. Stan code has been created and enhanced to actualize a censored system all through utilizing Stan technique. Moreover, parallel simulation tools are also implemented and additionally actualized with a broad utilization of rstan.