Information theory | Entropy and information
In probability theory, particularly information theory, the conditional mutual information is, in its most basic form, the expected value of the mutual information of two random variables given the value of a third. (Wikipedia).
How to determine if two events are mutually exclusive or not
👉 Learn how to find the probability of mutually exclusive events. Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive becau
From playlist Probability of Mutually Exclusive Events
Determining if two events are mutually exclusive or not
👉 Learn how to find the probability of mutually exclusive events. Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive becau
From playlist Probability of Mutually Exclusive Events
How to find the probability between two mutually exclusive events
👉 Learn how to find the probability of mutually exclusive events. Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive becau
From playlist Probability of Mutually Exclusive Events
Determine the probability of two events that are mutually exclusive
👉 Learn how to find the probability of mutually exclusive events. Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive becau
From playlist Probability of Mutually Exclusive Events
How to find the probability of mutually exclusive event with a die
👉 Learn how to find the probability of mutually exclusive events. Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive becau
From playlist Probability of Mutually Exclusive Events
David Sutter: "A chain rule for the quantum relative entropy"
Entropy Inequalities, Quantum Information and Quantum Physics 2021 "A chain rule for the quantum relative entropy" David Sutter - IBM Zürich Research Laboratory Abstract: The chain rule for the conditional entropy allows us to view the conditional entropy of a large composite system as a
From playlist Entropy Inequalities, Quantum Information and Quantum Physics 2021
Determining the truth of a conditional statement
👉 Learn how to determine the truth or false of a conditional statement. A conditional statement is an if-then statement connecting a hypothesis (p) and the conclusion (q). If the hypothesis of a statement is represented by p and the conclusion is represented by q, then the conditional stat
From playlist Conditional Statements
Determining the conditional probability from a contingency table
👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring
From playlist Probability
Nexus Trimester - Andrei Romashchenko (LIRMM)
On Parallels Between Shannon’s and Kolmogorov’s Information Theories (where the parallelism fails and why) Andrei Romashchenko (LIRMM) February 02, 2016 Abstract: Two versions of information theory - the theory of Shannon's entropy and the theory of Kolmgorov complexity - have manifest
From playlist Nexus Trimester - 2016 - Distributed Computation and Communication Theme
Nexus Trimester - Terence Chan (University of South Australia) - 1
Fundamental aspects of information inequalities 1/3 Terence Chan (University of South Australia) February 25, 2016 Abstract: Information inequalities are very important tools and are often needed to characterise fundamental limits and bounds in many communications problems such as data t
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
Pierre Baudot : Information Topology: Statistical Physic of Complex Systems and Data Analysis
Recording during the thematic meeting : "Geometrical and Topological Structures of Information" the August 29, 2017 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent
From playlist Geometry
Nexus Trimester - Galen Reeves (Duke University)
Understanding the MMSE of compressed sensing one measurement at a time Galen Reeves (Duke University) March 16, 2016 Abstract: Large compressed sensing problems can exhibit phase transitions in which a small change in the number of measurements leads to a large change in the mean-squared
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
Michael Kastoryano: "Classical restrictions of MPS are Gibbsian"
Entropy Inequalities, Quantum Information and Quantum Physics 2021 "Classical restrictions of MPS are Gibbsian" Michael Kastoryano - Amazon Abstract: We show that the norm squared amplitudes with respect to a local orthonormal basis (the classical restriction) of finite quantum systems o
From playlist Entropy Inequalities, Quantum Information and Quantum Physics 2021
Pierre Baudot (8/19/20): Cohomological characterization of information structures
Speaker: Pierre Baudot, Median Technologies. In collaboration in part with Daniel Bennequin, Monica Tapia, and Jean-Marc Goaillard Title: Cohomological characterization of information and higher order statistical structures - Machine learning and statistical physics aspects Abstract: We
From playlist AATRN 2020
A New Perspective on Holographic Entanglement by Matthew Headrick
11 January 2017 to 13 January 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru String theory has come a long way, from its origin in 1970's as a possible model of strong interactions, to the present day where it sheds light not only on the original problem of strong interactions, but
From playlist String Theory: Past and Present
Entropy-Based Bounds on Dimension Reduction in L_1 - Oded Regev
Oded Regev CNRS-ENS-Paris and Tel Aviv University November 28, 2011 For more videos, visit http://video.ias.edu
From playlist Mathematics
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 14
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: http://cs330.stanford.edu/fall2021/index.html To view all online courses and programs offered by Stanford, visit: http:/
From playlist Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn
Finding the conditional probability from a tree diagram
👉 Learn how to find the conditional probability of an event. Probability is the chance of an event occurring or not occurring. The probability of an event is given by the number of outcomes divided by the total possible outcomes. Conditional probability is the chance of an event occurring
From playlist Probability