Bayesian Statistics is an intensively logical and deeper concept in statistics, and holds a fair importance in data science, analytics, and artificial intelligence. AI❓ Really ❓ Yes❗ Today in #StatswithTeddy, we will learn about the Bayesian Inference, its importance, and its applications. We have learnt that AI today is basically Machine Learning, and ML is basically mathematics and statistics. So where does the Bayesian Statistics comes into play in today's AI and Data Science talks and future goals? Fundamentally, the ML models, or AI today are 'black box' models, which aren't easily understandable by us humans, and training of these models takes a lot of computational resources including the collection and storing of lots and lots of #DATA. We need millions of active users to our service to justify building a smart chatbot for us, and #chatgpt is an example of this. But are we always working on this big data? Was ChatGPT this massive on Day 01? No, right! We always start small and build upon that. But, these ML/AI models require such big datasets to work well. So, how do we start small? What tool do we have here to test whether the project is worth scaling or no? Here comes the #Bayesian_Inference, a part of our beloved Bayesian Statistics. This technique begins with us stating our prior beliefs about the system/model we are building, which allows us to encode expert opinion and domain expertise on how do we want the system to work, what is final goal. These beliefs are then combined with data to constrain the details of the model we are building. Then, at the time of prediction, we does not get one answer, but a series of answers along with the probability or a distribution of likely answers for us to assess risks and possibilities associated with the prediction. Some key points of Bayesian Inference are: 👉 It performs well with sparce data. Here, our ML/AI models fail. 👉It natively incorporated the idea of confidence 👉The model and results in this case are highly interpretable and easy to understand. Though the concept of Bayesian Inference in complex and hard to grasp, a new programming paradigm is available, to make things easier for us as #ProbabilisticProgramming. It hides the complexity of Bayesian inference, making the advanced techniques accessible to a broader audience. Applications of Bayesian Inference and Probabilistic Programming can be found in comments below. Future posts will discuss more on #ProbabilisticProgramming and #BayesianStatistics Till then, Stay Tuned! and follow Isha C. Keep Learning! Keep Growing! #BingeStats #datascience #probability #artificialintelligence #genai #mathematics #statistics #bayesian
Rajveer Raj
11mo
Great post! Bayesian Inference is indeed crucial in AI and Data Science. Its ability to handle sparse data, provide confidence, and offer interpretable results makes it invaluable in building and scaling ML/AI models. Looking forward to future posts.
LinkedIn Top Research Skill Voice | Data Science Specialist | Mathematician | AI Enthusiast
11moApplications of Bayesian Inference, and Probabilistic Programming involves the areas where we need to work on heterogeneous or noisy data, or anywhere we need a clear understanding of the uncertainties involved. Some domain where this concept can be applied for analysis, probabilistic modeling, and risk assessment are #ecommerce , #insuranceindustry #finance and #healthcare as these domains possess data science use cases which involves working with small, and noisy datasets which are often the sample of a large population.