Markov networks | Bayesian statistics
Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains.It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment.PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge.More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model.PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state).The "softening" of the logical formulas makes inference a polynomial time operation rather than an NP-hard operation. (Wikipedia).
Probabilistic logic programming and its applications - Luc De Raedt, Leuven
Probabilistic programs combine the power of programming languages with that of probabilistic graphical models. There has been a lot of progress in this paradigm over the past twenty years. This talk will introduce probabilistic logic programming languages, which are based on Sato's distrib
From playlist Logic and learning workshop
Introduction to Predicate Logic
This video introduces predicate logic. mathispower4u.com
From playlist Symbolic Logic and Proofs (Discrete Math)
Logic: The Structure of Reason
As a tool for characterizing rational thought, logic cuts across many philosophical disciplines and lies at the core of mathematics and computer science. Drawing on Aristotle’s Organon, Russell’s Principia Mathematica, and other central works, this program tracks the evolution of logic, be
From playlist Logic & Philosophy of Mathematics
Semantic models for higher-order Bayesian inference - Sam Staton, University of Oxford
In this talk I will discuss probabilistic programming as a method of Bayesian modelling and inference, with a focus on fully featured probabilistic programming languages with higher order functions, soft constraints, and continuous distributions. These languages are pushing the limits of e
From playlist Logic and learning workshop
Translating ENGLISH into PREDICATE LOGIC - Logic
In this video on Logic, we learn to translate English sentences into Predicate Logic. We do sentences with only constants and predicates, as well as introduce the universal and existential quantifier "some x is P" and "every x is P" and then do some practice problems. Predicate Logic trans
From playlist Logic in Philosophy and Mathematics
This first E-Lecture on Predicate Logic is meant as a gentle introduction. It first points out why propositional logic alone is not sufficient for the formalization of sentence meaning and then introduces the central machinery of predicate logic using several examples with which the studen
From playlist VLC103 - The Nature of Meaning
Introduction to Propositional Logic and Truth Tables
This video introduces propositional logic and truth tables. mathispower4u.com
From playlist Symbolic Logic and Proofs (Discrete Math)
Radek Adamczak: Functional inequalities and concentration of measure I
Concentration inequalities are one of the basic tools of probability and asymptotic geo- metric analysis, underlying the proofs of limit theorems and existential results in high dimensions. Original arguments leading to concentration estimates were based on isoperimetric inequalities, whic
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Lecture 24: Entanglement: QComputing, EPR, and Bell's Theorem
MIT 8.04 Quantum Physics I, Spring 2013 View the complete course: http://ocw.mit.edu/8-04S13 Instructor: Allan Adams In this lecture, Prof. Adams discusses the basic principles of quantum computing. No-cloning theorem and Deutsch-Jozsa algorithm are introduced. The last part of the lectur
From playlist 8.04 Quantum Physics I - Prof. Allan Adams
ML4Audio - pyctcdecode: A simple and fast speech-to-text prediction decoding algorithm
This week the Kensho team will join us to talk about pyctcdecode pyctcdecode is a fast and feature-rich CTC beam search decoder for speech recognition. Ask your questions in https://discuss.huggingface.co/t/ml-for-audio-study-group-pyctcdecode-jan-18/13561 Speakers - Raymond Grossman: R
From playlist Machine Learning for Audio
Making Decisions under Model Misspecification & Star-shaped Risk Measures - Maccheroni & Marinacci
Prof. Fabio Maccheroni & Prof. Massimo Marinacci - Making Decisions under Model Misspecification & Star-shaped Risk Measures Making Decisions under Model Misspecification (45min) Authors Simone Cerreia-Vioglio, Lars Peter Hansen, Fabio Maccheroni, Massimo Marinacci Abstract We use de
From playlist Uncertainty and Risk
Math 031 012017 Calculus I review; Fundamental Theorem of Calculus (no sound)
(Sorry - someone kicked the microphone off, so there's no sound.) Calculus I review: Extreme Value Theorem, definition of derivative, rules of differentiation (linearity, product rule, quotient rule, Chain Rule), Mean Value Theorem, antiderivative, indefinite integral, definite integrals.
From playlist Course 3: Calculus II (Spring 2017)
MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018 Instructor: Neha Narula View the complete course: https://ocw.mit.edu/MAS-S62S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61KHzhg3JIJdK08JLSlcLId Explanation of types of forks in blockchain technology
From playlist MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018
Calculus: Absolute Maximum and Minimum Values
In this video, we discuss how to find the absolute maximum and minimum values of a function on a closed interval.
From playlist Calculus
Deep Learning Lecture 10.2 - Logistic Regression
Probabilistic interpretation of Classification Logistic Regression Catergorial Cross-Entropy
From playlist Deep Learning Lecture
Alexey Bufetov (Bonn) -- Cutoff profile of ASEP on a segment
The mixing behavior of the Asymmetric Simple Exclusion Process (=ASEP) on a segment will be discussed. We will show that its cutoff profile is given by the Tracy-Widom distribution function, which extends earlier results of Labbe-Lacoin and Benjamini-Berger-Hoffman-Mossel. We will also dis
From playlist Columbia Probability Seminar
Richard Lassaigne: Introduction à la théorie de la complexité
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Mathematical Aspects of Computer Science
Introduction to Predicates and Quantifiers
This lesson is an introduction to predicates and quantifiers.
From playlist Mathematical Statements (Discrete Math)