By Avi Pfeffer
Useful Probabilistic Programming introduces the operating programmer to probabilistic programming. during this e-book, you will instantly paintings on sensible examples like construction a unsolicited mail filter out, diagnosing laptop approach information difficulties, and getting better electronic photos. you will find probabilistic inference, the place algorithms assist in making prolonged predictions approximately matters like social media utilization. alongside the best way, you are going to discover ways to use functional-style programming for textual content research, object-oriented types to foretell social phenomena just like the unfold of tweets, and open universe versions to gauge real-life social media utilization. The e-book additionally has chapters on how probabilistic types may also help in determination making and modeling of dynamic platforms.
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Extra resources for Practical Probabilistic Programming
1 Review of probabilistic reasoning essentials in chapter 1. As a reminder, general knowledge about a situation is encoded in the probabilistic model, while evidence provides specific information about a particular situation. An inference algorithm uses the model and the evidence to answer queries about your situation. Now let’s look at Figaro. 2 shows the key concepts of Figaro. 1. You express your general knowledge in the Figaro model. You provide specific knowledge about a situation in the form of evidence.
An inference algorithm uses the model and the evidence to answer queries about your situation. Now let’s look at Figaro. 2 shows the key concepts of Figaro. 1. You express your general knowledge in the Figaro model. You provide specific knowledge about a situation in the form of evidence. Queries tell the system what you’re interested in finding out. A Figaro inference algorithm takes the evidence and uses the model to provide answers to the queries. Now let’s look at each of these pieces in turn.
Therefore, a probabilistic program can be thought of as a program you randomly execute to generate an output. 8 illustrates this concept. In the figure, a probabilistic programming system contains a corner-kick program. This program describes the random process of generating the outcome of a corner kick. The program takes some inputs; in our example, these are the height of the center forward, the experience of the goalie, and the strength of the wind. Given the inputs, the program is randomly executed to generate outputs.