This presentation will provide an overview of advanced Bayesian exploration and its technical applications. We will discuss Bayesian methods for exploratory data analysis, predictive modeling, and other data science tasks.
Bayesian regression is a statistical method used to estimate the relationship between variables. It uses Bayesian inference, which is a form of statistical inference that relies on probability theory. In Bayesian regression, the relationships between variables are estimated through data rather than through assumptions.
Dynamic recommenders with Bayesian exploration are able to provide more accurate and personalized recommendations than traditional recommenders. By taking into account the user’s past behavior, the recommenders can adjust the recommendations to better fit the user.
Artificial Intelligence is changing the landscape of modern commerce. In our next session, we’ll interview Dr. Jay van Zyl, to discuss the practical applications of integrating AI technologies into your business. Dr. van Zyl begins by explaining why AI is essential to business, citing its ability to automate mundane tasks, identify patterns and trends, and [...]
Responding to customers requires an understanding of the ever-changing human contexts in which they exist. It is important to remain knowledgeable and aware of current trends, industry changes, and the needs of customers. We will discuss methods of monitoring customer responses to experiments using dashboards and activating further dynamic experimentation in ecosystem.Ai.
Dynamic experimentation for customer recommendations in real-time is an innovative approach to providing customers with tailored recommendations based on their individual preferences. ecosystem.Ai uses a combination of machine learning and computational social science to predict customer behavior and provide personalized recommendations.
Dynamic experimentation is a powerful tool that has the potential to revolutionize the way businesses interact with their customers. Join us as we take a brief look at dynamic experimentation, it's benefits and a few examples of successful applications. Click to learn more
In our last blog post we discussed the importance of creating novelty through experimentation to keep your communities engaged. Today we’re going to discuss the benefits of low code technology to help data scientists get these experiments and predictions into production faster. Why choose low-Code technology? To keep [...]
Some know it as a cold-start scenario, while others can just think of it as the end of A/B testing, we call it Dynamic Experimentation.
