Web Science/Part2: Emerging Web Properties/Generative Models for the Web/Introduction to generative modelling.webm

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Web Science/Part2: Emerging Web Properties

Web Science/Part2: Emerging Web Properties/MoocIndex

Web Science/Part2: Emerging Web Properties/Generative Models for the Web/Introduction to generative modelling.webm

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Introduction to generative modelling.webm

Learning goals

Video

Script

Quiz

Further reading

Discussion

Learning goals

1. Understand the principle methodology for building generative models
2. Remember why people are interested in generative models
3. Know why descriptive models are needed when evaluating a generative model
4. Be aware of one way to create a model for text generation

Video

Script

The slides can be found at File:Introduction-to-generative-modelling.pdf

Quiz

How to create a probability distribution from frequencies

	by going to relative frequencies
	by deviding each frequency with thte sum of all frequencies
	by aplying the gauss algorithm
	this is impossible

Further reading

1. tba

Discussion

Navigation

1. How big is the World Wide Web
   1. Problems with the question about the size of the Web
   2. 3 ways to study the Web
   3. A simplistic descriptive model
   4. An unrealistic, simplistic generative model
   5. Summary, further reading, homework
2. Simple statistical descriptive Models for the Web
   1. Counting Words And Documents
   2. Typical length of a document
   3. How to formulate a research hypothesis
   4. Number of words needed to understand most of Wikipedia
   5. Linguists way of checking simplicity of text
3. Advanced statistical descriptive models for the Web
   1. The Zipf law for text
   2. Visually straight lines on log log plots
   3. Fitting a curve on a log log plot
   4. Zipf law powerlaw or pareto law.webm
4. Modelling Similarity of Text
   1. Similarity Measures and their Applications
   2. Jaccard Similarity for Sets
   3. Cosine Similarity For Vectorspaces
   4. Probabilistic Similarity Measures Kullback Leibler Divergence
   5. Comparing Results of Similarity Merasures
5. Generative Models for the Web
   1. Introduction to generative modelling.webm
   2. Sampling from a probability distribution
   3. Evaluating a generative model
   4. Pittfalls when increasing the number of model parameters
6. Modeling the Web as a graph
   1. Reviewing terms from graph theory
   2. The standard web graph model
   3. Descriptive statistics of the web graph
   4. Topology of the web graph
   5. Modelling-graphs-with-linear-algebra

Sampling from a probability distribution