Probability: Modeling Uncertainty

[Audio] Good morning everyone. Today I will be discussing the various methods tools and techniques used in data analytics to address uncertainty such as Bayesian Inference Monte Carlo Simulation Bootstrapping Ensemble Learning Uncertainty Quantification in Neural Networks Probabilistic Graphical Models Interval Estimation Hypothesis Testing and Robust Statistics. Let's get started..

[Audio] Modeling uncertainty is a fundamental component of data analytics allowing us to make reliable decisions even when we are dealing with incomplete or noisy data. In this slide we will go through various methods tools and techniques to address uncertainty in data analytics with the aim to improve the accuracy of our data insights. Let's take a look..

Probabilistic Methods:.

[Audio] Monte Carlo Simulation utilizes numerical estimation strategies to simulate arbitrary examples from probability distributions achievable with the assistance of instruments like NumPy or matlab. Bayesian Inference displays vulnerability by joining earlier convictions and refreshing them with watched information with PyMC3 or Stan helping this in Python and R individually. The following slide will talk about Monte Carlo Simulation..

[Audio] Monte Carlo Simulation is a powerful mathematical technique used to model and evaluate uncertainty. By running multiple simulations it allows us to detect potential risks recognise opportunities and measure potential advantages and disadvantages of certain scenarios. Libraries such as NumPy or matlab are commonly used for conducting Monte Carlo simulations..

[Audio] Bootstrapping is a technique employed to understand variability and uncertainty in the data. It functions by resampling the dataset with replacement allowing for estimation of statistics. Implemented in languages such as Python with the help of scikit-learn and in R with boot library bootstrapping can help in discerning the variability of the dataset providing for constructing more accurate predictive models..

Machine Learning Techniques:.

[Audio] Ensemble learning is a technique that combines multiple predictive models to reduce uncertainty and improve prediction accuracy. Two of the most popular methods for doing this are Random Forests and Gradient Boosting Machines. These techniques are very reliable and often used in a wide range of applications for their strong performance..

[Audio] Bayesian neural networks and dropout regularization can be used to estimate uncertainty in predictions made by deep learning models. Additionally Probabilistic Graphical Models are highly effective for representing uncertain relationships between variables and performing probabilistic reasoning. Next we will examine this further..

[Audio] Probabilistic Graphical Models are a powerful tool for modeling and understanding uncertainty. They are capable of expressing more subtle probabilities than traditional mathematical models by taking into account relationships between variables. For instance when attempting to determine the probability of an event occurring relationships between potential causes and effects of the event can be considered to obtain a more precise prediction..

Statistical Analysis:.

[Audio] Interval Estimation is a powerful tool to model uncertainty providing us with a range of plausible values for population parameters as a confidence interval. This allows us to make decisions based on solid data with very little margin for error..

[Audio] When making decisions in the field of probability analysts often face a degree of uncertainty. To account for this uncertainty analysts use hypothesis testing which involves calculating the significance of the data to make informed decisions. This is done through the use of p-values and confidence levels. P-values represent the probability that the observed result is just due to chance while confidence levels reflect the degree of certainty of the analyst's decision. Together they help analysts make the right decisions while considering the uncertainty of the variables at play..

[Audio] Probability is an effective tool for tackling the issue of uncertainty. It allows us to assess a range of potential outcomes and their likelihood of occurring. This means we can use probabilities to evaluate and understand the potential effects of uncertainty and make informed decisions. When combined with simulation and statistical techniques probability has the power to influence data analysis and decision-making..

[Audio] I explored the concept of probability and how it is used to model uncertainty. We discussed how robust methods such as robust regression and trimmed means can be used to provide more reliable estimates when uncertainty is present. I thank you for your attention and I hope you now have a greater understanding of probability and how it can be used in a variety of situations..