By Kurt Busiek, Ben Abernathy
Every thing you want to learn about Astro urban and its many precise areas and faces: local publications, eating place innovations, highlights, neighborhood pursuits, advertisements for neighborhood companies, a heritage of town, a map or even a advisor to the who’s-who of the hero elite illustrated through fan-favorite artists.
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Extra resources for Astro City - A Visitor's Guide
For example, my shirt color and the temperature in Moscow are independent of each other: if you learn that I am wearing a blue shirt, then that provides no information about the current temperature in Russia. Zero correlation is one example of independence:2 if A is uncorrelated with B, then information about A does not help at all when trying to predict B. Alternately, nodes can be dependent: learning about A does help to predict B. For example, the temperature in Pittsburgh and the temperature in Moscow are dependent: learning that it is snowing in Pittsburgh is informative about the likely temperature range in Moscow.
This chapter introduces and explores the framework of graphical models, the computational and mathematical basis of that architecture. The main thread of the chapter focuses on a conceptual introduction, with mathematical details provided in sections marked with an asterisk (*); the reader can skip those sections with little loss of (qualitative) understanding. 1 explores the common elements shared by all graphical models, with subsequent sections examining the details of specific model types. As with many formal frameworks, a highlevel grasp of different graphical model types suffices to understand most uses of them.
To take a concrete example, consider associative models of contingency (or causal) learning, such as the Rescorla–Wagner (1972) model. At a high level, these models posit that people learn correlations (possibly including causal strengths) by updating associative strengths between various factors. Computationally, they use a two-step process: given a new case, one (i) predicts the state of some factors based on their associative connections with observed factors and then (ii) changes the associative strengths based on the error in that prediction.