'Multivariate gaussian' diaporamas de présentation

CS 59000 Statistical Machine learning Lecture 3

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Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 2: Probability distributions. General. Density estimation : Model probability distribution of a R.V. given a finite set of observations. Parametric distributions : Gaussian, Bernoulli, Multinomial, Exponential family.

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Midterm Review

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Local structures; Causal Independence, Context-sepcific independance

Local structures; Causal Independence, Context-sepcific independance. COMPSCI 276 Fall 2007. Reducing parameters of families. Determinizm Causal independence Context-specific independanc Continunous variables. Causal Independence.

562 views • 43 slides

Experiment-driven System Management

Experiment-driven System Management. Shivnath Babu Duke University Joint work with Songyun Duan, Herodotos Herodotou, and Vamsidhar Thummala. Managing DBs in Small to Medium Business Enterprises (SMBs). Peter is a system admin in an SMB Manages the database (DB)

518 views • 41 slides

Experiment-driven System Management

Experiment-driven System Management. Shivnath Babu Duke University Joint work with Songyun Duan, Herodotos Herodotou, and Vamsidhar Thummala. Managing DBs in Small to Medium Business Enterprises (SMBs). Peter is a system admin in an SMB Manages the database (DB)

416 views • 41 slides

Fingerprint-based

Fingerprint-based. Maria Papadopouli Associate Professor University of Crete, FORTH, and KTH http://www.ics.forth.gr/mobile/ mgp@ics.forth.gr. Joint research with D . Milioris , A. Papakonstantinou G . Tzagkarakis , and Panos Tsakalides University of Crete and FORTH.

394 views • 39 slides

A gentle introduction to Gaussian distribution

A gentle introduction to Gaussian distribution. X = 0. X = 1. X: Random variable. Review. Random variable Coin flip experiment. P(x). P(x) >= 0. 0. 1. x. Review. Probability mass function (discrete). Any other constraints? Hint: What is the sum?. Example: Coin flip experiment.

389 views • 21 slides

A gentle introduction to Gaussian distribution

A gentle introduction to Gaussian distribution. X = 0. X = 1. X: Random variable. Review. Random variable Coin flip experiment. P(x). P(x) >= 0. 0. 1. x. Review. Probability mass function (discrete). Any other constraints? Hint: What is the sum?. Example: Coin flip experiment.

564 views • 21 slides

Neural Firing

Neural Firing. Notation I. x(t)=signal vector; N(t)=#spikes fired up to time t; H(k)=[? 1:k-1 ,x 1:k ,N 1:k ] ? t[k],t[k]+ ?t[k] =likelihood over interval t k , t k +?t k,i ?t k,i ~ interval: t k +? i=1:j-1 ?t k,j , t k + ? i=1:j ?t k,j ,. Fact.

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Generating Multivariate Gaussian

Generating Multivariate Gaussian. Generating Multivariate Gaussian. We require n normal random numbers drawn from a multivariate normal distribution with. Generating Multivariate Gaussian. Generate n independent, zero-mean, unit variance normal random variables

289 views • 9 slides