Statistical modeling = applying statistical analysis to dataset
Statistical model = math relationship b/w random & non-random variables
Statistical inference = using data analysis to infer properties of underlying probability distribution
Statistical modeling = applying statistical analysis to dataset
Statistical model = math relationship b/w random & non-random variables
Statistical inference = using data analysis to infer properties of underlying probability distribution
user
# Types of Statistical Models

## Parametric Models

- Assumes that the data comes from a specific probability distribution
- Example: Normal distribution
- Advantage: Can make strong inferences about the data
- Disadvantage: Assumptions may not always be valid

## Non-Parametric Models

- Does not assume a specific probability distribution
- Example: Histogram
- Advantage: Can be used when the data does not follow a specific probability distribution
- Disadvantage: Cannot make as strong inferences about the data as parametric models

## Supervised Learning Models

- Used for classification and regression tasks
- Requires a training dataset with labeled examples
- Example: Logistic regression, decision trees, support vector machines
- Advantage: Can learn from the training dataset and make predictions on new data
- Disadvantage: Requires a large amount of training data and can overfit to the training dataset

## Unsupervised Learning Models

- Used for clustering and dimensionality reduction tasks
- Does not require labeled examples
- Example: K-means clustering, principal component analysis
- Advantage: Can find patterns in the data without the need for labeled examples
- Disadvantage: Cannot make predictions on new data and may not always find the optimal solution

## Generative Models

- Used for generating new data that follows a specific probability distribution
- Example: Generative Adversarial Networks (GANs)
- Advantage: Can generate new data that is similar to the training dataset
- Disadvantage: Can be difficult to train and may not always generate high-quality data

## Discriminative Models

- Used for classification tasks
- Focuses on distinguishing between different classes of data
- Example: Logistic regression, decision trees, support vector machines
- Advantage: Can make accurate predictions on new data
- Disadvantage: Cannot generate new data and may not always find the optimal solution

## Probabilistic Models

- Used for modeling uncertainty and randomness in the data
- Example: Bayesian networks, Markov chains
- Advantage: Can incorporate prior knowledge and uncertainty into the model
- Disadvantage: Can be difficult to train and may not always provide accurate predictions

## Deterministic Mod
assistant
Statistical modeling = applying statistical analysis to dataset
Statistical model = math relationship b/w random & non-random variables
Statistical inference = using data analysis to infer properties of underlying probability distribution
user
Statistical modeling
assistant
Statistical modeling = applying statistical analysis to dataset
Statistical model = math relationship b/w random & non-random variables
Statistical inference = using data analysis to infer properties of underlying probability distribution
user
# Types of Statistical Models

## Parametric Models

- Assumes that the data comes from a specific probability distribution
- Example: Normal distribution
- Advantage: Can make strong inferences about the data
- Disadvantage: Assumptions may not always be valid

## Non-Parametric Models

- Does not assume a specific probability distribution
- Example: Histogram
- Advantage: Can be used when the data does not follow a specific probability distribution
- Disadvantage: Cannot make as strong inferences about the data as parametric models

## Supervised Learning Models

- Used for classification and regression tasks
- Requires a training dataset with labeled examples
- Example: Logistic regression, decision trees, support vector machines
- Advantage: Can learn from the training dataset and make predictions on new data
- Disadvantage: Requires a large amount of training data and can overfit to the training dataset

## Unsupervised Learning Models

- Used for clustering and dimensionality reduction tasks
- Does not require labeled examples
- Example: K-means clustering, principal component analysis
- Advantage: Can find patterns in the data without the need for labeled examples
- Disadvantage: Cannot make predictions on new data and may not always find the optimal solution

## Generative Models

- Used for generating new data that follows a specific probability distribution
- Example: Generative Adversarial Networks (GANs)
- Advantage: Can generate new data that is similar to the training dataset
- Disadvantage: Can be difficult to train and may not always generate high-quality data

## Discriminative Models

- Used for classification tasks
- Focuses on distinguishing between different classes of data
- Example: Logistic regression, decision trees, support vector machines
- Advantage: Can make accurate predictions on new data
- Disadvantage: Cannot generate new data and may not always find the optimal solution

## Probabilistic Models

- Used for modeling uncertainty and randomness in the data
- Example: Bayesian networks, Markov chains
- Advantage: Can incorporate prior knowledge and uncertainty into the model
- Disadvantage: Can be difficult to train and may not always provide accurate predictions

## Deterministic Mod
assistant