Tags
Language
Tags
May 2024
Su Mo Tu We Th Fr Sa
28 29 30 1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28 29 30 31 1

Markov Logic: An Interface Layer for Artificial Intelligence

Posted By: AvaxGenius
Markov Logic: An Interface Layer for Artificial Intelligence

Markov Logic: An Interface Layer for Artificial Intelligence by Pedro Domingos
English | PDF | 2009 | 155 Pages | ISBN : 1598296922 | 1.3 MB

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit.

Bayesian Econometrics

Posted By: AvaxGenius
Bayesian Econometrics

Bayesian Econometrics by Mauro Bernardi
English | PDF | 2020 | 148 Pages | ISBN : 3039437852 | 9.4 MB

Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics.

An Introduction to Sequential Monte Carlo

Posted By: AvaxGenius
An Introduction to Sequential Monte Carlo

An Introduction to Sequential Monte Carlo by Nicolas Chopin
English | PDF,EPUB | 2020 | 390 Pages | ISBN : 3030478440 | 30 MB

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics.