Suppose that we have two independent binomial random variables X \sim Bin(n,p_x) and Y \sim Bin(m,p_y). Find the MLE for p under the assumption that p = p_x = p_y. X1, ..., Xn is a random sample from N (0, sigma^2). Find ...
이전 댓글 표시 Sara Kadam2023년 4월 25일 0 링크 번역 댓글:Star Strider2023년 4월 26일 채택된 답변:Star Strider I have preprocessed the image to a grayscale image. I want to find the Maximum Likelihood of the image with maximun accuracy, ca...
The American StatisticianNorton R M.The double exponential distribution:Using calculus to find a maximum likelihood estimator. The American Statistician . 1984Norton, R. M. (1984). The double exponential distribution: Using calculus to find a maximum likelihood estimator. The American Statistician, 38...
Let X1, X2, , Xn be an iid random sample of size n from an Exponential ( ) distribution with probability density function Find the maximum likelihood estimator for , . Then using that result, calculate the estimate when x...
Let f(x; theta) = (1/theta) x (1 - theta)/0, 0 less than x less than 1, 0 < theta less than infinity. a) Show that the maximum likelihood estimator of theta is theta = -(1/n) sigma_i = 1^n ln X_i b...
This is a problem if, as we believe, the model does not function as its proponents suggest it does, and can produce highly misleading results. The debate also mirrors and complements that taking place elsewhere regarding another APC model called the Intrinsic Estimator (see Pelzer et al. 2015...
Find the maximum likelihood estimator for alpha. A random variable Y has cdf G_Y (y) = 10y^9 - 9y^{10} on 0 <= y <= 1(a) Calculate the expected value and variance of the random variable Y (b) Calculate E (Y^{-9})....
The maximum increase in predictive power provided by data augmentation is reached when the training data is replicated one time. Therefore, extending the original training data with one perturbed repetition thereof represents a reasonable trade-off between the increased performance of the models and the...
In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost. DADA relaxes the discrete DA policy selection to a differentiable optimization problem via Gumbel-Softmax. In addition, we introduce an unbiased gradient estimator, RELAX, leading to an ...
Profile likelihood confidence intervals are a robust alternative to Wald's method if the asymptotic properties of the maximum likelihood estimator are not met. However, the constrained optimization problem defining profile likelihood confidence intervals can be difficult to solve in these situations, ...