A statistical culture that helps modeling real time phenomenon in more interpretable ways. Based on a more pragmatic probabilistic thinking, Bayesian principles are highly useful in decoding uncertainty. Learning from the past is an interesting aspect in any stochastic modeling which is naturally integrated in Bayesian tool kits. Whether it is a requirement of sequential learning or predictive learning, Bayesian philosophy provides a systematic way to handle from the data and the past records or historical facts or elicited consensus from experts. Today’s computing power makes your data analysis and modeling ideas to take off in a sophisticated manner and reporting with the state-of-the-art tools including customized user interface and experience. With a blend of philosophy, science, computation, and technology, at ThinkLabz Bayesian learning will be a more rewarding scope and experience for your Data Science Practices.

• Know the different Statistical Learning Cultures for Data Modeling
• Know the Bayesian Basic Tool Kits and Components
• Understand computation Engines in R and Python
• Understand the application in variety of Industries - Pharmacy, Clinical Trials, Marketing, Economics and Electronic Commerce, Medical / Life Sciences and more

• Statistical Thinking; Objective and Subjective probabilities
• Theoretical distributions; summaries
• Posterior and prior distributions
• Monte Carlo simulation
• Markov Chain Monte Carlo simulations
• Probabilistic Programming, STAN, PyMC3, TensorFlow Probability, RSTAN, PySTAN
• Adaptive Designs, Multilevel modeling, Pharmaceuticals, Clinical Trial designs, Life and Bio sciences, Psychology

Bayesian is an amazing tool to use in field of probability. It is often used in the field of machine learning. Its use in probability framework to fit a model to a specific dataset and to develop models for predictive modelling is well acclaimed

With Bayesian Theory you can use it in the calculation on real time scenario along with Monte Carlo Simulation . Monte Carlo simulation can be used to tackle a range of problems in domains such as Banking & Finance, Production & Engineering and Clinical Trial Reseaerch.