This implies an assumption that the probability of each sample is independent from the others. Select one: A. True B. False
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This implies an assumption that the probability of each sample is independent from the others.
Select one:
A. True
B. False
![In the MLE formulation of the classification problem, we have a likelihood function as the following:
n
L(0) = ||PC»®]x®,0)
i=1](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbb462ed3-78dd-484d-8be6-2ec47fabc29e%2F9b3e1198-76b6-4584-ac42-b68297b9ae82%2Fnrysy1r_processed.png&w=3840&q=75)
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- Can you please follow up on the question and answer the second part: 'Propose a transformation of this likelihood function whose maximum is the same and can be computed easily'it is known that a natural. law obeys the quadratic relationship y=ax^2.what is the best line of form y=px+q that can be used to model data and minimise Mean-squared-error, if all of the data points are drawn uniformly at random from the domain [0,1]?This is an Uncertainty problem. Given the attached table of some probabilities values of a ‘Medical Knowledge-based System’, evaluate ALL posterior probabilities of event, ‘Heart-Attack’ with given hypothesis. Hint: Use Bayesian Theorem
- the logit function(given as l(x)) is the log of odds function. what could be the range of logit function in the domain x=[0,1]?ANY help would be greatly appreciated. From 1965 to 1974, in U.S. there were M= 17,857,857 male livebirths and F= 16,974,194 female livebirths. We model the number of male livebirth as a binomial distribution withparameterssize = M+F and prob = p. The following code computes the maximum likelihood estimator for p. male = 17857857 female = 16974194 ll <-function(p){dbinom(male, size = male+female, prob=p, log=TRUE) } ps <-seq(0.01, 0.99, by = 0.001) ll.ps <-ll(ps) plot(ps, ll.ps, type='l') phat <- ps[which.max(ll.ps)] abline(v = phat, col='blue') QUESTION: For this problem, can you give a theoretical formula for the maximum likelihood estimator,ˆp, usingMandF? (No need to compute the numerical value.)We are intrested in predicting the percentage of people commuting to work by walking given some input variables. Each observation corresponds to a different city and each input variable summarizes some characteristic of a given city, such as density, urban sprawl and average income per capita. This is 1. not a machine learning problem. Only social scientists would be interested in such a problem. 2. both a classification and a regression problem as it depends on the way one codes the output variable as either 0, 1 or a a particular number in the [0,1] interval. 3. a regression problem. The output variable is continuous. 4. a classification problem. Walking to work is a discrete variable and can only take two values: to walk to work and not to walk to wor
- Define the Perfect Bayesian Equilibrium (PBE).can we conclude S from the following premisesP=>QP=>Rnegation(Q^R)SvPConsider values shown in the table below:i=1 (cold) i=2 (allergy) i=3 (stomach pain) p(Hi)0.60.30.1 p(E1 |Hi)0.30.80.3 p(E2 |Hi)0.60.90.0Those values represent (hypothetically) three mutually exclusive and exhaustive hypotheses for the patient’s condition. For example, H1: the patient has a cold, H2: the patient has an allergy, and H3: the patient has stomach pain with their prior probabilities, p(Hi)’s and two conditionally independent pieces of evidence (E1, patient sneezes and E2, patient coughs) which support these hypotheses to differing degrees. Therefore;a) Compute the posterior probabilities for the hypothesis if the patient sneezes. What is the conclusion that can be derived from this condition?b) Based on the answer from the previous result, as the patient coughs are now observed, compute the posterior probabilities for this condition. Explain the results.
- The average running time of the worst case is determined using amortised analysis. True FalsePlease solve the following problem. Quiz = Pass Quiz = Fail AI = Fail 0.1 0.2 AI = Pass 0.6 0.1 Mid = Pass Mid = Fail AI = Fail 0.2 0.2 AI = Pass 0.5 0.1 Suppose you have three events AI Grade, Quiz, and Mid. Here each event has two possible outcomes, either pass or fail. Additionally, given that AI Grade is observed, Quiz and Mid become independent of each other. Also, out of every 100 students, 30 students fail the AI course. Now, using the joint probability tables given, calculate P(AI Grade=Pass, Quiz=Fail, Mid=Fail).A confusion matrix based on one particular cut-off probability can determine the entire ROC curve. True False