1973
DOI: 10.1016/0021-9991(73)90127-7
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Numerical analytic continuation using Padé approximants

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Cited by 10 publications
(8 citation statements)
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“…Due to the independence of A + and A − , S(A + ) and S(A − ) follow the same functional form as the conventional entropy, Eq. (7), that stems from the Poisson distribution. Thus,…”
Section: Positive-negative Memmentioning
confidence: 99%
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“…Due to the independence of A + and A − , S(A + ) and S(A − ) follow the same functional form as the conventional entropy, Eq. (7), that stems from the Poisson distribution. Thus,…”
Section: Positive-negative Memmentioning
confidence: 99%
“…Just as the two Poisson distributions of the respective balls lead to a Skellam distribution by integrating out the additional degree of freedom, a reduction of the parameter space from A + and A − to A = A + − A − leads to an entropy S ± (A) that differs from the conventional entropy in Eq. (7). The derivation of S ± (A) was first carried out in the context of cosmic microwave background radiation [36][37][38], an available software package providing this entropy is memsys5 [45].…”
Section: Positive-negative Memmentioning
confidence: 99%
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“…Many different methods to perform the AC have been proposed, e.g., series expansions such as the Padé method [5], machine learning [6], stochastic methods [7][8][9][10][11][12][13], and the maximum entropy method (MEM) [14][15][16][17][18]. The latter is a consistent approach as it is based on Bayesian probability theory; however, a highly ignorant entropic prior is used, which merely accounts for positivity and additivity of the reconstructed signal.…”
Section: Introductionmentioning
confidence: 99%
“…Over the last decades a large set of tools was developed to deal with this problem, among which we find the Padé approximation [127,128], the maximum entropy method [129][130][131][132][133], deconvolution procedures [134], singular value decomposition based algorithms [135], machine learning methods [136], sparse modeling approaches [137], and stochastic sampling techniques [138][139][140][141][142][143]. All the above methods have their advantages and drawbacks but in this section we will concentrate only on the Padé approximation because we expect the noise in our data to be negligible since the only constraint we have is sufficient resolution of the numerical objects which we can assure by increasing the number of k-points N and Matsubara frequencies N mats .…”
Section: Analytic Continuationmentioning
confidence: 99%