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+influence+graph+or+causal+graph+of+a+high-dimensional+multivariate+discrete-time+Markov+process+with+memory.+At+any+discrete+time+instant%252C+each+observed+variable+of+the+multivariate+process+is+a+binary+string+of+random+length%252C+which+is+parameterized+by+an+unobservable+or+hidden+%5b0%252C1%5d-valued+scalar.+The+hidden+scalars+corresponding+to+the+variables+evolve+according+to+discrete-time+linear+stochastic+dynamics+dictated+by+the+underlying+influence+graph+whose+nodes+are+the+variables.+We+extend+an+existing+algorithm+for+learning+i.i.d.+graphical+models+to+this+Markovian+setting+with+memory+and+prove+that+it+can+learn+the+influence+graph+based+on+the+binary+observations+using+logarithmic+(in+number+of+variables+or+nodes)+samples+when+the+degree+of+the+influence+graph+is+bounded.+The+crucial+analytical+contribution+of+this+work+is+the+derivation+of+the+sample+complexity+result+by+upper+and+lower+bounding+the+rate+of+convergence+of+the+observed+Markov+process+with+memory+to+its+stationary+distribution+in+terms+of+the+parameters+of+the+influence+graph.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.b3903e1610d4635f53d029e4681deeac%3frik%3dXokV5leYgyj2Jg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Memory+and+Learning+Research+Graph&FORM=IRPRST&ck=B7C61690EC813E95B2BFE8F7A041C368&selectedIndex=24&itb=0)
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