Probability

In the book “the Signal and the noise”, author Nate Silver talks about prediction from many different angles. Silver explains how prediction is a part of our everyday life and how it affects us. From math to history, inside of a class room or on a court/field, prediction is something we deal with on a day to day basis unconsciously. Silver talks about the benefits of failure and how failure is helpful in the long run with making predictions. Throughout the years we have made progress with…

Health Insurance Probability Act otherwise known as HIPAA was set in place in 1996 to make sure patient medical information is protected and safe. In today's world keeping medical information protected is extremely important especially that technology is ruling the world and people know how to access people personal information by hacking computer systems. Working in the human service or health field HIPAA and PHI should be taken seriously because working with clients we have to make sure that…

rationale of an undefined parameter [9]. This results in system dynamics being graded higher in achieving the project objectives. However, the execution of Bayesian network is easier when done in software because it involves the calculation of the probability of a certain scenario occurring [9], while system dynamics on the other hand attempts to recognise the principal and manner of a system [5]. System dynamics obtained an overall score of 0.08 while Bayesian network got -0.08. Therefore,…

they would like to reach. Eminem was made in the projects of Eastern Detroit. He was tormented and mistreated by other student bullies in the school he attended. He drove to accomplish on what he was aiming for and best part is he was against the probabilities. Because of that hard work he is one of the best white rappers of all time. Marshall Mathers now known as Slim Shady or more commonly known as Eminem. He was born in Detroit in 1974. From there he moved to Kansas to find a better life.…

is an exact probability model for the number of automobile accidents over the past year for the 50 medical interns? (Note: The 11 reported accidents include both police-reported and non-police-reported accidents). Answer:- Fixed Proportion = .065 Number of interns involved in accident last year = total number of interns*.065. n = .065*It where n denoted interns in an accident and It denotes the total number of interns. P(n) = ItCn*.065^n*.935^(It-n)......from .binomial probability…

List the probability value for each possibility in the binomial experiment that was calculated in MINITAB with the probability of a success being ½. (Complete sentence not necessary) P(x=0) P(x=6) P(x=1) P(x=7) P(x=2) P(x=8) P(x=3) P(x=9) P(x=4) P(x=10) P(x=5) 4. Give the probability for the following based on the MINITAB calculations with the probability of a success being ½. (Complete sentence not necessary) P(x?1) P(x<0) P(x>1) P(x?4) P(4 5. Calculate the mean and standard…

necessary statistical parameters, such as mean covariance and prior probabilities, from the training data set. We modeled two discriminant functions, which were further used on test data to discriminate between the two classes. We assumed that all the data are normally distributed. Introduction: Sample Size Selection: Sample is the representative…

the links are Q=2mn. The rewiring algorithm includes Q steps, and at each step i, link i are chosen and rewired to a node randomly chosen over the graph with the intra-modular rewiring probability P, provided that multi-link and self-loops are prohibited. The probability P is the intra-modular rewiring probability in this modular network.…

speed of the mean reversion. θ is the mean value of the variance. σ is the volatility of the variance. These three are positive constants. Ws(t) and Wv(t) are Brownian motion variables, and ρ is the correlation coefficient between them. In some probability measure, we assume dWs(t) * dWv(t) = ρdt. For computational convenient, it is general to employee logarithm to transform the asset price process S(t) into X(t) = Log (S(t)), and apply Itô’s Lemma(3) to equations (1). (3) We can get following…

As we model this Markov process, we observe the state-dependent output, yet initially we are not able to note any of the states. Each state has a unique probability distribution over each possible output. Thus, information about the sequence of states through which the model makes its way can be obtained from the sequence of outputs generated, while the rest of the model remains hidden. The algorithm tracks…