Data Science in R: Building Probabilistic Models with bnlearn, igraph, and gRain Packages

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Probabilistic models are a class of statistical models that represent uncertainty and variability in data using probability distributions. These models are widely used in various fields, including statistics, machine learning, and artificial intelligence. The primary goal of probabilistic modeling is to capture and quantify uncertainty in the relationships between variables. Here’s an overview of probabilistic models:

1. Key Concepts and Components:

a. Probability Distributions:

• Probabilistic models are built upon probability distributions, which describe the likelihood of different outcomes. Common distributions include normal, binomial, Poisson, and more.

b. Random Variables:

• Variables in probabilistic models are often treated as random variables. These variables can take on different values with associated probabilities.

c. Parameters:

• Parameters define the characteristics of the probability distribution. In a normal distribution, for example, mean and variance are parameters.

d. Likelihood:

• The likelihood function…