True or false and show work, please.
The answer to the "likelihood in the setting above" is that the estimate of pt = ct / n (generally pi = ci / n).
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True or false and show work, please.
The answer to the "likelihood in the setting above"...
A random variable X has probability density function f(x)=(a-1)x^(-a),for x>=1. (a) For independent observations x1,...,xn show...
A random variable X has probability density function f(x)=(a-1)x^(-a),for x>=1. (a) For independent observations x1,...,xn show that the log-likelihood is given by, l(a;x1,...,xn)=nlog(a-1)-a (b) Hence derive an expression for the maximum likelihood estimate for ↵. (c) Suppose we observe data such that n = 6 and 6 i=1 log(xi) = 12. Show that the associated maximum likelihood estimate for ↵ is given by aˆ ↵ =1 .5. logri We were unable to transcribe this image
please work out parts b,c,d with clear steps thanks A mixture of m univariate Gaussians has...
please work out parts b,c,d with clear steps thanks
A mixture of m univariate Gaussians has the PDF: X(x) - where each pi 0 and Σ-i pi-1, and N(x; μ, σ*) = (2πσ2)-1/2 exp (-(x-p?/(2σ2)) exp (-(x-μ)2 a) How many parameters does a mixture of m Gaussians have? b) Let xi, , Vn be n observations drawn from a mixture of m Gaussians. Write down the log-likelihood function. Hint: it should involve two summations c) Let 1 k < m....
2. According to genetic theory, there are three blood types MM, NM and NN which should...
2. According to genetic theory, there are three blood types MM, NM and NN which should occur in a very large population with probabilities, O, (1-9)3 and 30(1-0), where is the (unknown) gene frequency, 0 s 0S1. (Note: This distribution is for exercise purposes only and may not be true in real life.) (a) Suppose that in a random sample of size n = 10 from the population, there are f, f, and fs of types MM, NM and NN...
Really short question! Please help me to solve, thank you! (10%)Q3 (Logistic regression): We collected n...
Really short question! Please help me to solve, thank
you!
(10%)Q3 (Logistic regression): We collected n 15 independent binary observations : i- 1, , 15) and their corresponding covariates {xi : і = 1, , 15). Assume the relationship between yi and zi (for i = 1, , 15) is Vi ~ Bernoulli(p.) and logit(Pi)-α+82i, where logit(t) = log ti. Please 1) write down the likelihood function L(a, B|x, y) of the logistic regression model; 2) derive the Newton method...
Please show all work X, be a random sample from the distribution with the probability density...
Please show all work
X, be a random sample from the distribution with the probability density function Let A0 and let X, X2, f(x; A) 24xe, x>0. a. Find E(X), where k> -8. Enter a formula below. Use* for multiplication, for divison, ^ for power, lam for A, Gamma for the r function, and pi for the mathematical constant . For example, lam k*Gamma(k/2)/pi means Akr(k/2)/T Ax2 or u =x2. Hint 1: Consider u -e"du Hint 2: I'(a) a 0...
please answer these three questions thank you! (e) Given that f(n) € O(n) and g(n) e...
please answer these three questions thank you!
(e) Given that f(n) € O(n) and g(n) e O(n log n), please formally prove that f(n) + g(n) € O(nº). [4 (6) We know that kn is in O(n) for any constant k. Is the following claim correct? Briefly explain. I kn = ŻO(n) = O(n?) 13 o f is a function that satisfies the following: • f is in O(n), . f is in 2(1), • f is neither in e(1)...
in a Bayesian view. Consider the prior π(a)-1 for all a e R Consider a Gaussian linear model Y = aX+ E Determine whether each of the following statements is true or false. π(a) a uniform prior. (1) (...
in a Bayesian view. Consider the prior π(a)-1 for all a e R Consider a Gaussian linear model Y = aX+ E Determine whether each of the following statements is true or false. π(a) a uniform prior. (1) (a) True (b) False L(Y=y14=a,X=x) (2) π(a) is a jeffreys prior when we consider the likelihood (where we assume xis known) (a) True (b)False Y-XB+ σε where ε E R" is a random vector with Consider a linear regression model E[ε1-0, E[eErJ-1....
Page 3 Name (please print) III (10) When solving this problem show all the steps needed...
Page 3 Name (please print) III (10) When solving this problem show all the steps needed to transform the expressions into ones that can be found in the table and indicate the entry of the table used in each step. a) Find the Laplace transform F(s) of the function (3-24 + 5e") sin(Tt) b) Find the inverse Laplace transform f(t) of the function F(s) = 9 32 +8-20 S()--'{F(s) 1. i Table of Laplace Transforms F(x) = {/0) (1)-2-(F) F(s)-...
I need your help ASAP! please show all work for this activity sheet f True/False Write...
I
need your help ASAP! please show all work for this activity sheet
f True/False Write T for true, F for false a) Prostaglandins are used to make steroid hormones b) Prostaglandins are synthesized only in the liver c) Prostaglandins can be used to induce labor d) Prostaglandins are synthesized in the cell only when it is needed e) Prostaglandins prevent the inflammation response Prostaglandins can cause fever and pain sensitivity g) Phospholipids are the only lipids found in a...
Can you please help me answer Task 2.b? Please show all work. fs=44100; no_pts=8192; t=([0:no_pts-1]')/fs; y1=sin(2...
Can you please help me answer Task 2.b?
Please show all work.
fs=44100; no_pts=8192;
t=([0:no_pts-1]')/fs;
y1=sin(2*pi*1000*t);
figure;
plot(t,y1);
xlabel('t (second)')
ylabel('y(t)')
axis([0,.004,-1.2,1.2]) % constrain axis so you can actually see
the wave
sound(y1,fs); % play sound using windows driver.
%%
% Check the frequency domain signal. fr is the frequency vector and
f1 is the magnitude of F{y1}.
fr=([0:no_pts-1]')/no_pts*fs; %in Hz
fr=fr(1:no_pts/2); % single-sided spectrum
f1=abs(fft(y1)); % compute fft
f1=f1(1:no_pts/2)/fs;
%%
% F is the continuous time Fourier. (See derivation...
please work out parts b,c,d with clear steps thanks
A mixture of m univariate Gaussians has the PDF: X(x) - where each pi 0 and Σ-i pi-1, and N(x; μ, σ*) = (2πσ2)-1/2 exp (-(x-p?/(2σ2)) exp (-(x-μ)2 a) How many parameters does a mixture of m Gaussians have? b) Let xi, , Vn be n observations drawn from a mixture of m Gaussians. Write down the log-likelihood function. Hint: it should involve two summations c) Let 1 k < m....
2. According to genetic theory, there are three blood types MM, NM and NN which should occur in a very large population with probabilities, O, (1-9)3 and 30(1-0), where is the (unknown) gene frequency, 0 s 0S1. (Note: This distribution is for exercise purposes only and may not be true in real life.) (a) Suppose that in a random sample of size n = 10 from the population, there are f, f, and fs of types MM, NM and NN...
Really short question! Please help me to solve, thank
you!
(10%)Q3 (Logistic regression): We collected n 15 independent binary observations : i- 1, , 15) and their corresponding covariates {xi : і = 1, , 15). Assume the relationship between yi and zi (for i = 1, , 15) is Vi ~ Bernoulli(p.) and logit(Pi)-α+82i, where logit(t) = log ti. Please 1) write down the likelihood function L(a, B|x, y) of the logistic regression model; 2) derive the Newton method...
Please show all work
X, be a random sample from the distribution with the probability density function Let A0 and let X, X2, f(x; A) 24xe, x>0. a. Find E(X), where k> -8. Enter a formula below. Use* for multiplication, for divison, ^ for power, lam for A, Gamma for the r function, and pi for the mathematical constant . For example, lam k*Gamma(k/2)/pi means Akr(k/2)/T Ax2 or u =x2. Hint 1: Consider u -e"du Hint 2: I'(a) a 0...
please answer these three questions thank you!
(e) Given that f(n) € O(n) and g(n) e O(n log n), please formally prove that f(n) + g(n) € O(nº). [4 (6) We know that kn is in O(n) for any constant k. Is the following claim correct? Briefly explain. I kn = ŻO(n) = O(n?) 13 o f is a function that satisfies the following: • f is in O(n), . f is in 2(1), • f is neither in e(1)...
in a Bayesian view. Consider the prior π(a)-1 for all a e R Consider a Gaussian linear model Y = aX+ E Determine whether each of the following statements is true or false. π(a) a uniform prior. (1) (a) True (b) False L(Y=y14=a,X=x) (2) π(a) is a jeffreys prior when we consider the likelihood (where we assume xis known) (a) True (b)False Y-XB+ σε where ε E R" is a random vector with Consider a linear regression model E[ε1-0, E[eErJ-1....
Page 3 Name (please print) III (10) When solving this problem show all the steps needed to transform the expressions into ones that can be found in the table and indicate the entry of the table used in each step. a) Find the Laplace transform F(s) of the function (3-24 + 5e") sin(Tt) b) Find the inverse Laplace transform f(t) of the function F(s) = 9 32 +8-20 S()--'{F(s) 1. i Table of Laplace Transforms F(x) = {/0) (1)-2-(F) F(s)-...
I
need your help ASAP! please show all work for this activity sheet
f True/False Write T for true, F for false a) Prostaglandins are used to make steroid hormones b) Prostaglandins are synthesized only in the liver c) Prostaglandins can be used to induce labor d) Prostaglandins are synthesized in the cell only when it is needed e) Prostaglandins prevent the inflammation response Prostaglandins can cause fever and pain sensitivity g) Phospholipids are the only lipids found in a...
Can you please help me answer Task 2.b?
Please show all work.
fs=44100; no_pts=8192;
t=([0:no_pts-1]')/fs;
y1=sin(2*pi*1000*t);
figure;
plot(t,y1);
xlabel('t (second)')
ylabel('y(t)')
axis([0,.004,-1.2,1.2]) % constrain axis so you can actually see
the wave
sound(y1,fs); % play sound using windows driver.
%%
% Check the frequency domain signal. fr is the frequency vector and
f1 is the magnitude of F{y1}.
fr=([0:no_pts-1]')/no_pts*fs; %in Hz
fr=fr(1:no_pts/2); % single-sided spectrum
f1=abs(fft(y1)); % compute fft
f1=f1(1:no_pts/2)/fs;
%%
% F is the continuous time Fourier. (See derivation...
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