Naive Bayes
++ Using Naive Bayes classification, we can calculate the conditional probability of each road type hypothesis given the observed features. +
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+ + + + + + ++ This project focuses on developing a probabilistic model for road type identification, utilizing Naive Bayes and Bayesian Estimation. +
++ The primary goal is to distingush between different road types based on image data, with a model trained on a set of observed hypotheses. + The Naive Bayes Classifier is applied for straightforward classification by calculating the probability of each road type hypothesis given the observed features. In contrast, Bayesian Estimation is used to handle sequential observations, allowing for dynamic state updates and predictions over time. +
++ To enhance model robustness, Laplacian Smoothing is applied to avoid zero-probability issues for unseen observations. + Through simulation, I demonstrate the effectiveness of probabilistic inference in accurately identifying road types, which can be applied to improve decision-making processes in autonomous systems. +
++ Using Naive Bayes classification, we can calculate the conditional probability of each road type hypothesis given the observed features. +
+ ++ Using sequential observations and dynamilly update the system's state, we can predict the road type over time. +
+ + ++ The hypothesis space for different road types, such as +
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