{"responseHeader":{"status":0,"QTime":7,"params":{"q":"{!q.op=AND}id:\"102883\"","hl":"true","hl.simple.post":"","hl.fragsize":"5000","fq":"!embargo_tdt:[NOW TO *]","hl.fl":"ocr_t","hl.method":"unified","wt":"json","hl.simple.pre":""}},"response":{"numFound":1,"start":0,"docs":[{"file_name_t":"Soller-Automated_Detection.pdf","thumb_s":"/a0/59/a059ad00057c6327edb5653e83c4b263dd45cb5f.jpg","oldid_t":"compsci 10996","setname_s":"ir_computersa","restricted_i":0,"format_t":"application/pdf","modified_tdt":"2016-05-26T00:00:00Z","file_s":"/8a/4f/8a4f0e07a6b618df9f4d3e6a8241c3920e2194e3.pdf","title_t":"Page 126","ocr_t":"[A- Ail]xi = 0. A.2 Basic Statistical Definitions and Nomenclature 111 (A.12) Let X be a multidimensional random variable with dimensionality p of patient observations. The variable P is the number of observed response variables and N is the number of observations. The probability distribution function, F(x) = P(X ~ x), is the probability that X is less than or equal to some value x in its domain. The probability density function is f( x) = d~~x). In the univariate case, the expected value of X, the first moment of X [118], is defined as E(X) = j_: xf(x )dx and the variance, the second moment [118] of X, is Var(X) = j_: [X- E(x )]2 f(x )dx = j_: x2 f(x)dx- [E(X)] 2 = E(X2 )- [E(X)] 2 • The standard deviation of X is O\"x = +Jvar(X). (A.13) (A.14) (A.15) The notation is extended to the multivariate case. The expected value of X is","id":102883,"created_tdt":"2016-05-26T00:00:00Z","parent_i":102961,"_version_":1642982670145159168}]},"highlighting":{"102883":{"ocr_t":[]}}}