slides - University of California, Berkeley
... Probability that a message is eventually received successfully without any need for retransmission is greater than 0.98 ...
... Probability that a message is eventually received successfully without any need for retransmission is greater than 0.98 ...
Chapter 2: Fundamentals of the Analysis of Algorithm Efficiency
... • Expected number of basic operations considered as a random variable under some assumption about the probability distribution of all possible inputs ...
... • Expected number of basic operations considered as a random variable under some assumption about the probability distribution of all possible inputs ...
Visualizing and Exploring Data
... the arguments for the use of P absurd. They amount to saying that a hypothesis that may or may not be true is rejected because a greater departure from the trial value was improbable; that is, that is has not predicted something that has not happened.” ...
... the arguments for the use of P absurd. They amount to saying that a hypothesis that may or may not be true is rejected because a greater departure from the trial value was improbable; that is, that is has not predicted something that has not happened.” ...
Document
... Chapter 1 Introduction Definition of Algorithm An algorithm is a finite sequence of precise instructions for performing a computation or for solving a problem. Example 1 Describe an algorithm for finding the maximum (largest) value in a finite sequence of integers. Solution ...
... Chapter 1 Introduction Definition of Algorithm An algorithm is a finite sequence of precise instructions for performing a computation or for solving a problem. Example 1 Describe an algorithm for finding the maximum (largest) value in a finite sequence of integers. Solution ...
linear-system
... are based on quantities of the matrix A that are hard to compute and difficult to compare with convergence estimates of other iterative methods (see e.g. [2, 3, 6] and the references therein). What numerical analysts would like to have is estimates of the convergence rate with respect to standard qu ...
... are based on quantities of the matrix A that are hard to compute and difficult to compare with convergence estimates of other iterative methods (see e.g. [2, 3, 6] and the references therein). What numerical analysts would like to have is estimates of the convergence rate with respect to standard qu ...
Chapter 2: Fundamentals of the Analysis of Algorithm
... It cannot be investigated the way the previous examples are. ...
... It cannot be investigated the way the previous examples are. ...
1 What is the Subset Sum Problem? 2 An Exact Algorithm for the
... In iteration i, we compute the sums of all subsets of {x1 , x2 , ..., xi }, using as a starting point the sums of all subsets of {x1 , x2 , ..., xi−1 }. Once we find the sum of a subset S’ is greater than t, we ignore that sum, as there is no reason to maintain it. No superset of S’ can possibly be ...
... In iteration i, we compute the sums of all subsets of {x1 , x2 , ..., xi }, using as a starting point the sums of all subsets of {x1 , x2 , ..., xi−1 }. Once we find the sum of a subset S’ is greater than t, we ignore that sum, as there is no reason to maintain it. No superset of S’ can possibly be ...
n - Physics
... In order to do this comparison we need to know A, the total number of intervals (=20). Since A is calculated from the data, we give up one degree of freedom: dof=m-1=5-1=4. If we also calculate the mean of our Possion from the data we lose another dof: dof=m-1=5-2=3. Example: We have 10 data points. ...
... In order to do this comparison we need to know A, the total number of intervals (=20). Since A is calculated from the data, we give up one degree of freedom: dof=m-1=5-1=4. If we also calculate the mean of our Possion from the data we lose another dof: dof=m-1=5-2=3. Example: We have 10 data points. ...
The Elements of Statistical Learning
... Step 2: Similarly, generate 10 means from the from the bivariate Gaussian distribution N((0,1)T,I) and label this class RED Step 3: For each class, generate 100 observations as follows: l ...
... Step 2: Similarly, generate 10 means from the from the bivariate Gaussian distribution N((0,1)T,I) and label this class RED Step 3: For each class, generate 100 observations as follows: l ...
Weighted Estimation for Analyses with Missing Data Cyrus Samii Motivation
... to maximize efficiency (Robins and Rotnitzky, 1995; Tsiatis, 2006). Carpenter et al (2006) present an example of linear regression with three covariates, (X1,X2,X3), and missingness on X1. They derive an augmented estimating equation, with Si = Xi(Yi − β 0Xi) and φi = E [Xi(Yi −β 0Xi)|Yi, Xi2, Xi3], ...
... to maximize efficiency (Robins and Rotnitzky, 1995; Tsiatis, 2006). Carpenter et al (2006) present an example of linear regression with three covariates, (X1,X2,X3), and missingness on X1. They derive an augmented estimating equation, with Si = Xi(Yi − β 0Xi) and φi = E [Xi(Yi −β 0Xi)|Yi, Xi2, Xi3], ...
Document
... Here each measured data point (yi) is allowed to have a different standard deviation (si). The LS technique can be generalized to two or more parameters for simple and complicated (e.g. non-linear) functions. u One especially nice case is a polynomial function that is linear in the unknowns (ai): ...
... Here each measured data point (yi) is allowed to have a different standard deviation (si). The LS technique can be generalized to two or more parameters for simple and complicated (e.g. non-linear) functions. u One especially nice case is a polynomial function that is linear in the unknowns (ai): ...
Algorithm 1.1 Sequential Search Problem Inputs Outputs
... for (j=0; j < n; j++) { // something of O(1) } // end for ...
... for (j=0; j < n; j++) { // something of O(1) } // end for ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.