R Through Python Eyes Part 5 Statistical Modeling Medium
In this post, we will cover Matsuura K. (2023) Bayesian Statistical Modeling with Stan, R, and Python. The MCMC sample from the posterior distribution is saved in the fit object (an object of class CmdStanMCMC), and it can be easily drawn by using draws function: The returned result, d_ms is a data frame with the size of (number of draws) × (number of the variables). By default, draws from the warmup period are not included. As an example, let us check the draws of the slope b and compute its 95% Bayesian confidence interval.
We can use quantile function to compute the 95% interval because d_ms$b is a numeric vector: Let us first generate the draws from these distributions: ideally, we want to compute the various quantities in Stan. transformed parameters block and generated quantities block can be used for this purpose At this point we are ready to discuss the functional programming paradigm in R and Python. The other important paradigm, Object-Oriented, will be discussed in the following Chapter.
R and Python are powerhouses for statistical modeling and data visualization. They offer libraries like ggplot2, Matplotlib, and Seaborn for creating stunning graphics. These tools let you turn raw data into eye-catching visuals that tell a story. But it's not just about pretty pictures. These languages also pack a punch when it comes to statistical analysis. From regression and ANOVA to time series and machine learning, R and Python have got you covered.
They're your Swiss Army knives for tackling complex data problems.
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In This Post, We Will Cover Matsuura K. (2023) Bayesian
In this post, we will cover Matsuura K. (2023) Bayesian Statistical Modeling with Stan, R, and Python. The MCMC sample from the posterior distribution is saved in the fit object (an object of class CmdStanMCMC), and it can be easily drawn by using draws function: The returned result, d_ms is a data frame with the size of (number of draws) × (number of the variables). By default, draws from the war...
We Can Use Quantile Function To Compute The 95% Interval
We can use quantile function to compute the 95% interval because d_ms$b is a numeric vector: Let us first generate the draws from these distributions: ideally, we want to compute the various quantities in Stan. transformed parameters block and generated quantities block can be used for this purpose At this point we are ready to discuss the functional programming paradigm in R and Python. The other...
R And Python Are Powerhouses For Statistical Modeling And Data
R and Python are powerhouses for statistical modeling and data visualization. They offer libraries like ggplot2, Matplotlib, and Seaborn for creating stunning graphics. These tools let you turn raw data into eye-catching visuals that tell a story. But it's not just about pretty pictures. These languages also pack a punch when it comes to statistical analysis. From regression and ANOVA to time seri...
They're Your Swiss Army Knives For Tackling Complex Data Problems.
They're your Swiss Army knives for tackling complex data problems.
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