Web Science/Part2: Emerging Web Properties/Generative Models for the Web

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Web Science/Part2: Emerging Web Properties

Web Science/Part2: Emerging Web Properties/Generative Models for the Web

Web Science/Part2: Emerging Web Properties/MoocIndex

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Generative Models for the Web

Learning goals

Associated units

Further reading

Discussion

Learning goals

Associated units

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Introduction to generative modelling.webm

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

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Sampling from a probability distribution

Understand how to sample values from an arbitrary probability distribution

Have seen yet another application of the cumulative distribution function

Understand that sampling from a distribution is just a coordinate transformation of the uniform distribution

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Evaluating a generative model

See that it makes sense to compare statistics

Understand that comparing statistics is not a well defined task

Be aware of the fact that very different models could lead to the same statistics

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Pittfalls when increasing the number of model parameters

See that one can always increase the model parameters

Know that increasing model parameters often yields a more accurate model

Be aware of the bigram and mixed models as examples for our generative processes

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Further reading

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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