1 Introduction  
2 Overview of Generative Models  
2.1 Gaussian Mixture Models  
2.2 Kernel Density Estimators  
2.3 Markov Chain Models  
2.4 Bayesian Networks  
2.5 Genetic Algorithms  
2.6 Boltzmann Machines  
2.7 Autoencoders  
2.8 Neural Autoregressive Distribution Estimators  
2.9 Sparse Coding  
2.10 Recurrent Neural Networks  
2.11 Convolutional Neural Networks  
2.12 Transformers  
2.13 Generative Adversarial Networks  
2.14 Generative Moment Matching Networks  
2.15 Plug & Play Generative Networks  
2.16 Copulas  
2.17 Normalizing Flow Models  
2.18 Reinforcement Learning  
2.19 Diffusion Models  
2.20 Virtual Environments  
3 Classification of Generative Models  
3.1 Criteria for Classification  
3.2 Data Evaluation and Trend Analysis  
3.3 Metadata  
3.4 Data Structure  
3.5 Data Types  
3.6 Sampling Requirements  
3.7 Sampling Process  
3.8 Training Process  
3.9 Data Sets  
3.10 Model Performance  
3.11 Privacy  
3.12 Summary  
3.13 Guideline for Synthetic Data Generation  
4 Related Work  
4.1 Domain- or Model-Specific Overviews and Comparisons  
4.2 Comprehensive Reviews  
5 Conclusion