This September, I started my MSc at UBC SPPH with a goal of becoming a researcher on clinical trials. During the past semester, I discovered I love it more than I could imagine. I enjoy everything that I am doing, especially my research which I can work until 3 am in the morning without feeling tired or exhausted. I count myself as a fortunate person as I have found what I love, unlike many other people who spend their entire life looking for what they like. However, as a person who wants to…
always uncertain. According to JCGM 200, measurement uncertainty is defined as follows: " non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used, according to the probability distribution function" [3]. No measurement result is quite accurate and reliable, unless it is reported with uncertainty [4, 5]. Because of uncertainty in measurement, there is always the risk of mistaken decisions about that whether it is…
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.…
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…
5. Hopf-Andronov-Poincare bifurcation In this section, we shall show that the system (2) undergoes a Hopf-Andronov-Poincare bifurcation by using as a bifurcation real parameter. Without loss of generality, suppose that is a function of and . Then system (2) becomes (29) with . System (29) can be written as (30) where , and is the bifurcation real parameter. The function is a on an open set in . Let be the set of equilibria of system…
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…
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,…
results that are almost certain. The Law of Large Numbers is “A law expressing the fact that if a trial in which all outcomes are independent of each other and equally likely is repeated, then the relative frequency of each outcome approximates its probability with increasing accuracy as the number of repetitions becomes sufficiently large” is the definition given for law of large numbers (Law of Large Numbers). The application of weak law of large…
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…
position offsets from the INS position each time it is started. Point Mass Filter (PMF) PMF is a calculation that determines the position probability density function (PDF), as said by (N. Bergman, 1999). The point mass filter estimator the priori position distributions together with the error models of the depth measurements and the map to produce a posteriori probability…