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[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml Demonstration of PCA and other dimensional reduction Two class case: \omega_1 and \omega_2 with p(x|\omega_1) = \left| 2 \pi \mathbf{\Sigma}_1 \right|^{-\frac{1}{2}} e^{-\frac{1}{2} \left( \mathbf{x} – \mathbf{\mu}_1 \right)^{t} \mathbf{\Sigma}_1\!^{-1} \left( \mathbf{x} – \mathbf{\mu}_1 \right) } with \mathbf{\mu}_1 = \mathbf{0} and \mathbf{\Sigma}_1=\mathbf{I} , with p(x|\omega_2) = \left| 2 \pi […]

[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml Demonstration of PCA and other dimensional reduction Four class case: \omega_1 , \omega_2 , \omega_3 and \omega_4 with p(x|\omega_k) = \left| 2 \pi \mathbf{\Sigma}_k \right|^{-\frac{1}{2}} e^{-\frac{1}{2} \left( \mathbf{x} – \mathbf{\mu}_k \right)^{t} \mathbf{\Sigma}_k\!^{-1} \left( \mathbf{x} – \mathbf{\mu}_k \right) } with \mathbf{\mu}_1 = \left[ \begin{array}{c} 1 \\ 1 \\

[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml Examine what happens as the number of dimensions increases. We have two classes \omega_1 and \omega_2 . For both classes, we will select the length r signal vector signal \mathbf{x} as p(x|\omega_k) \sim N \left( \mathbf{\mu}_k , \mathbf{\Sigma} \right) where \mathbf{\mu}_1 = \mathbf{0} , \mathbf{\mu}_2 = \mathbf{1}

[Content_Types].xml _rels/.rels matlab/document.xml matlab/output.xml metadata/coreProperties.xml metadata/mwcoreProperties.xml metadata/mwcorePropertiesExtension.xml metadata/mwcorePropertiesReleaseInfo.xml % Sketch p(x|ω1) and p(x|ω2) x=-10:1e-3:8; pxw1=(1/sqrt(8*pi))*exp(-1/8*(x+3).^2); pxw2=(1/3*exp(1/3*(x-3))).*(x3); plot(x,pxw1,x,pxw2); legend(‘p(x | \omega_1)’,’p(x | \omega_2)’) grid; %(a) Classify the source class as ω1 or ω2 when P(ω1) = P(ω2) fpxw1= @(x) (1/sqrt(8*pi))*exp(-1/8*(x+3).^2); fpxw2= @(x) (1/3*exp(1/3*(x-3))).*(x

International Macroeconomics 6: The Asset Approach, Dynare, and Computational Copyright By PowCoder代写 加微信 powcoder Steady States Graded Homework Problems∗ Francisco E. Ilabaca, Ph.D. Johns Hopkins University November 1, 2022 1. Consider the following planning problem (all non-price variables are normalized by the aggregate population), where all notation is standard: yt = Z̄ · ztnαt In

CS/SE 4X03 — Assignment 4 Ned Nedialkov 29 March 2021 Due date: 12 April 2021 Problem 1 [9 points] Study sections 1 to 4 and 6 of https://arxiv.org/abs/1801. 05894 and also the corresponding lecture slides. Unzip the file NN_code.zip [2 points] Modify the netbp.m code so you can pass parameters as follows function cost =