- To provide a solid understanding of the theoretical and practical foundations of Bayesian inference in the context of machine learning. - To introduce students to the design, construction and analysis of Bayesian networks. - To train in the use of advanced computational methods-including Hamiltonian Monte Carlo (HMC), INLA, Variational Bayes (VB) for solving real-world problems. - Develop skills in data analysis through the techniques taught in the course - Introduce and apply Bayesian spatial analysis in multidisciplinary contexts. - Integrate theory with practice through theoretical/practical activities in the use of RStudio and associated tools (R, RStan and eventually TensorFlow), encouraging critical debate and discussion of practical cases.

K4: Know the models and methods of statistical analysis for both static and dynamic data K9: now the existing models in cost management and apply them to any production process and the main instruments of accounting management for decision making, with the aim of obtaining an integrated vision of the operational, organizational and behavioral contexts in which accounting information systems are used for senior management. K10: now the fundamental elements of the organizational structure and the factors that influence the design of organizations, understanding and analyzing how the objectives of the organization affect the results, and the definition and planning of the strategies that will guarantee the achievement of these objectives K12: now how to identify or be able to create the statistical or probabilistic model appropriate to the specific problem arising in each business activity (finance, marketing, production planning and control, etc.). C1: Develop and master interpersonal skills on initiative, responsibility, conflict resolution and negotiation, which are essential in the professional environment. C4: Ability to develop and validate statistical models that help to address and solve problems relevant to today's society. C6: Ability to interpret the results of quantitative analysis, prepare clear reports and communicate conclusions effectively, using advanced data analysis tools. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S5: Manipulate computationally and analytically the established models, taking advantage of the power of statistical methods, optimization, etc., and perform the analysis of the results obtained.

1. Introduction to Bayesian Networks 2. Construction and analysis of simple Bayesian networks in R. 3. Advanced Bayesian Networks 4. Advanced Computational Methods: Hamiltonian Monte Carlo (HMC) and Introduction to INLA; Variational Bayes (VB) and Parallel MCMC. 5. Cluster Analysis with Mixture Models 6. Dynamic Linear Models (DLMs) 7. Bayesian Spatial Analysis

The sessions will be developed in a theoretical/practical format through the use of the computer, using the RStudio environment (https://www.rstudio.com) and R language (https://www.r-project.org). The teacher will indicate the packages to be used, based on RStan (https://mc-stan.org/users/interfaces/rstan) and, where appropriate, on TensorFlow (https://www.tensorflow.org). After explaining the content of Bayesian inference applied to a specific real problem, the possible solution of the case will be illustrated by means of a practical demonstration in R, emphasising the critical elements of the solution and promoting discussion of the results obtained with the students.