<![CDATA[Combining observed data with previous knowledge, Bayesian inference is a strong statistical technique for parameter estimation. Parameters are seen as random variables; previous opinions are updated using Bayes' theorem to generate the posterior distribution. By means of this approach, model parameters can be uncertain and change with additional data. Still, calculating the posterior analytically is sometimes impossible, mostly in complex models with high dimensional data. Markov Chain Monte Carlo (MCMC) methods, such as Metropolis Hastings and Gibbs sampling, use repeated processes produce samples from the posterior distribution thereby addressing this issue. Varitional inference offers a faster, deterministic option by approximating the posterior with a simpler distribution. The Laplace approximation uses local curvature for a Gaussian approximation. Common uses of these methods are statistics and machine learning for parameter estimation, model selection, and uncertainty analysis. The study evaluates each approach's effectiveness, showing that MCMC offers the best accuracy but variational inference and Laplace approximations offer quicker but less precise substitutes. The results emphasize the importance of choosing the appropriate method depending on the complexity of the data and the computational efficiency]]>
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