Probability: Modeling Uncertainty

[Audio] Good morning everyone and welcome to this presentation. Today we will be looking at how Princess Garvie Camingawan approaches modeling uncertainty with innovative methods tools and techniques used in data analytics. These include Bayesian Inference Monte Carlo Simulation Bootstrapping Ensemble Learning Uncertainty Quantification in deep learning Probabilistic Graphical Models Interval Estimation Hypothesis Testing and Robust Statistics. Let us begin..

[Audio] With uncertainty being a source of confusion and difficulty when it comes to data analytics efforts it is essential to incorporate methods tools and techniques that will help in modeling and managing uncertainty. This will help us better understand our data and make more reliable predictions. By recognizing the presence of uncertainty and properly managing it we can have more confidence in our results and the accuracy and reliability of them..

Probabilistic Methods:.

[Audio] Considering how Monte Carlo simulations can be used to estimate uncertainty Bayesian inference utilizes prior beliefs and observed data to model it. This is made easier by tools like PyMC3 and Stan in both Python and R..

[Audio] Monte Carlo simulation is a technique used by scientists to evaluate uncertainty. It works by drawing samples from probability distributions and using them to estimate potential results. NumPy and matlab are two common tools for this task. This allows us to gain a better understanding of the chances of various events happening and to make predictions accordingly..

[Audio] Bootstrapping is an effective method for modeling uncertainty as it samples data with replacement to accurately calculate statistics. Libraries such as scikit-learn and boot in R provide the ability to leverage this powerful methodology..

Machine Learning Techniques:.

[Audio] Ensemble learning is a powerful tool for predictive modeling. It involves combining multiple models together for the purpose of reducing uncertainty and improving the accuracy of predictions. Some widely used examples are Random Forests and Gradient Boosting Machines. When used properly Ensemble learning is a great way to boost model performance..

[Audio] Neural networks with Bayesian inference or dropout regularization can be used to evaluate the certainty of predictions made by deep learning models. Probabilistic graphical models are used to portray unpredictable relationships between different variables and can be used for probabilistic inference..

[Audio] Probabilistic graphical models are a powerful way of representing the relationship between multiple variables when there is uncertainty involved. By using these models we can more accurately consider the different factors at play and account for the uncertainty that comes with each factor. This makes them invaluable in probabilistic reasoning..

Statistical Analysis:.

[Audio] Confidence intervals are a useful means of estimating population parameters enabling us to quantify the degree of uncertainty around our assessments. By utilizing confidence intervals we can establish a scope of likely values for a population parameter. This scope can then be applied to analyze results and reach sound decisions..

[Audio] Hypothesis testing is a useful technique in making decisions and understanding the uncertainty around them. It enables analysts to measure the impact of uncertainty when formulating assumptions and drawing conclusions. Through generating p-values and confidence thresholds it offers a dependable way to assess and contrast the probability that various outcomes may be accurate..

[Audio] When it comes to data analytics uncertainty is unavoidable. To analyze data effectively and make sound decisions it is essential to recognize and manage the uncertainty that comes with it. Achieving this requires the use of a combination of probabilistic methods simulations and robust statistical approaches to identify and reduce the effect of uncertainty..

[Audio] Robust statistics are a powerful tool for making decisions when facing uncertainty. Such methods like robust regression and trimmed means are created to tolerate extreme values for more reliable outcomes in unpredictable scenarios. Basically robust statistics permit us to make wiser decisions when confronted with uncertainty..

Time Series Forecasting:.

[Audio] Structural time series models or state space models also known as Bayesian models offer a powerful approach to probabilistic forecasting providing estimates of uncertainty alongside predictions. This type of approach can help to yield a better understanding of how uncertainty affects predictions making them invaluable for businesses seeking to make data-driven decisions..

[Audio] Seasonal adjustment is a must when studying a time series. The X-13-ARIMA-SEATS model is a widely used model that can be applied to recognize and adjust for seasonal trends considering the natural unstableness of seasonality. This model can be a really helpful resource for research..

[Audio] Humans have endeavored to elucidate and foresee results in circumstances where vulnerability is the standard. Through the utilization of information visualizations we can all the more effectively create a comprehension of likelihood and its job in such conditions. Likelihood can be hard to comprehend however by breaking it into its segments we can acquire a superior comprehension and decide the most probable outcomes. Graphs outlines and maps can assist us with visualizing expansive measures of information permitting us to recognize examples and draw ends. By exploiting information visualizations we can more readily decipher the multifaceted nature of likelihood..

[Audio] Uncertainty is a fact that cannot be overlooked. Visualization tools can be immensely helpful in making us comprehend the uncertainty better. Error bars highlighted areas and probabilistic density charts are fantastic ways to make the data uncertainty and output doubts more palpable. By utilizing these strategies we can better evaluate our opportunities of victory in any given situation..

[Audio] Sensitivity analysis allows us to better comprehend how various input parameters can affect the output of a system. A sensitivity plot or tornado diagram can provide an understanding of the magnitude of the changes thus enabling us to forecast the consequences of different inputs on the system. This knowledge is essential for modelling the unpredictability of a system..

Decision Making:.

[Audio] Making decisions can be difficult with uncertainty surrounding potential outcomes. One effective way to consider all options is to utilize decision trees or decision matrices to weigh the options and examine the associated probabilities of each result. This creates a better understanding of the decision-making process and provides the means to make more informed selections..

[Audio] We appreciate your attendance and your attention to this presentation about probability and modeling uncertainty. We demonstrated how Monte Carlo simulation and decision trees can be employed to analyze risk during situations of uncertainty by going through different possibilities. We trust that you now have a greater comprehension of how to use these tactics to assess likely risks. Thank you all for joining and listening..