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A trend stationary process is not strictly stationary, but can easily be transformed into a stationary process by removing the underlying trend, which is solely a function of time.

rting. In the latter case of a deterministic trend, the process is called a trend stationary process, and stochastic shocks have only transitory effects after which the variable tends toward a deterministically evolving (non-constant) mean. <span>A trend stationary process is not strictly stationary, but can easily be transformed into a stationary process by removing the underlying trend, which is solely a function of time. Similarly, processes with one or more unit roots can be made stationary through differencing. An important type of non-stationary process that does not include a trend-like behavior is

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Importantly, a complicated covariance function can be defined as a linear combination of other simpler covariance functions in order to incorporate different insights about the data-set at hand.

{\displaystyle \nu } and Γ ( ν ) {\displaystyle \Gamma (\nu )} is the gamma function evaluated at ν {\displaystyle \nu } . <span>Importantly, a complicated covariance function can be defined as a linear combination of other simpler covariance functions in order to incorporate different insights about the data-set at hand. Clearly, the inferential results are dependent on the values of the hyperparameters θ (e.g. ℓ and σ) defining the model's behaviour. A popular choice for θ is to provide maximum a pos

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Novelist Adam Langer skewers the publishing trade — and some of its recent trends — while digging toward something deeper.

30+ years Terms of Service Privacy Policy YOU ARE HERE: LAT Home→Collections Book review: 'The Thieves of Manhattan' by Adam Langer <span>Novelist Adam Langer skewers the publishing trade — and some of its recent trends — while digging toward something deeper. July 18, 2010|By Ella Taylor, Special to the Los Angeles Times Email Share The Thieves of Manhattan A Novel Adam Langer Spiegel & Grau: 260 pp., $15 paper

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n \in \mathbb{R}^Dxn∈RD. We aim to represent each xn\mathbf{x}_nxn under a latent variable zn∈RK\mathbf{z}_n \in \mathbb{R}^Kzn∈RK with lower dimension, K< DK##BAD TAG##\mathbf{W}W relates the latent variables to the data. <span>Specifically, we assume that each latent variable is normally distributed, zn∼N(0,I).\mathbf{z}_n \sim N(\mathbf{0}, \mathbf{I}).zn∼N(0,I). The corresponding data point is generated via a projection, xn∣zn∼N(Wzn,σ2I),\mathbf{x}_n \mid \mathbf{z}_n \sim N(\mathbf{W}\mathbf{z}_n, \sigma^2\mathbf{I}),xn∣zn∼N(Wzn,σ2I), where the matrix W∈RD×K\mathbf{W}\in\mathbb{R}^{D\times K}W∈RD×K are known as the principal axes. In probabilistic PCA, we are typically interested in estimating the principal axes W

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Karhunen–Loève theorem - Wikipedia Karhunen–Loève theorem From Wikipedia, the free encyclopedia (Redirected from Karhunen–Loeve expansion) Jump to: navigation, search In the theory of stochastic processes, the Karhunen–Loève theorem (named after Kari Karhunen and Michel Loève), also known as the Kosambi–Karhunen–Loève theorem [1] [2] is a representation of a stochastic process as an infinite linear combination of orthogonal functions, analogous to a Fourier series representation of a function on a bounded interval. The transformation is also known as Hotelling Transform and Eigenvector Transform, and is closely related to Principal Component Analysis (PCA) technique widely used in image processing

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ia (Redirected from Orthogonal function) Jump to: navigation, search In mathematics, orthogonal functions belong to a function space which is a vector space (usually over R) that has a bilinear form. <span>When the function space has an interval as the domain, the bilinear form may be the integral of the product of functions over the interval: ⟨ f , g ⟩ = ∫ f ( x ) g ( x ) d x . {\displaystyle \langle f,g\rangle =\int f(x)g(x)\,dx.} The functions f and g are orthogonal when this integral is zero: ⟨ f , g ⟩ = 0. {\displaystyle \langle f,\ g\rangle =0.} As with a basis of vectors in a finite-dimensional space, orthogonal functions can form an infinite basis for a function space. Suppose {f n }, n = 0, 1, 2, … is a sequence of orthogonal functions. If f n has positive support then ⟨ f n

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esar (as imperial heir) Imperator Caesar Marcus Aurelius Antoninus Augustus (upon joint ascension, with Lucius Verus, to the throne [4] ) Dynasty Antonine Father Marcus Annius Verus Antoninus Pius (adoptive) Mother Domitia Lucilla <span>Marcus Aurelius (/ɔːˈriːliəs/; Latin: Marcus Aurelius Antoninus Augustus; [6] [notes 1] [9] 26 April 121 – 17 March 180 AD) was Roman emperor from 161 to 180, ruling jointly with Lucius Verus until Verus' death in 169 and jointly with his son, Commodus, from 177. He was the last of the so-called Five Good Emperors. He was a practitioner of

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Marcus Aurelius ( / ɔː ˈ r iː l i ə s / ; Latin: Marcus Aurelius Antoninus Augustus ; [6] [notes 1] [9] 26 April 121 – 17 March 180 AD) was Roman emperor from 161 to 180

esar (as imperial heir) Imperator Caesar Marcus Aurelius Antoninus Augustus (upon joint ascension, with Lucius Verus, to the throne [4] ) Dynasty Antonine Father Marcus Annius Verus Antoninus Pius (adoptive) Mother Domitia Lucilla <span>Marcus Aurelius (/ɔːˈriːliəs/; Latin: Marcus Aurelius Antoninus Augustus; [6] [notes 1] [9] 26 April 121 – 17 March 180 AD) was Roman emperor from 161 to 180, ruling jointly with Lucius Verus until Verus' death in 169 and jointly with his son, Commodus, from 177. He was the last of the so-called Five Good Emperors. He was a practitioner of

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ors on Hilbert space 3 Unbounded operators 4 Quaternion polar decomposition 5 Alternative planar decompositions 6 Numerical determination of the matrix polar decomposition 7 See also 8 References Matrix polar decomposition[edit source] <span>The polar decomposition of a square complex matrix A is a matrix decomposition of the form A = U P {\displaystyle A=UP\,} where U is a unitary matrix and P is a positive-semidefinite Hermitian matrix. [1] Intuitively, the polar decomposition separates A into a component that stretches the space along a set of orthogonal axes, represented by P, and a rotation (with possible reflection) represented by U. The decomposition of the complex conjugate of A {\displaystyle A} is given by A ¯ = U ¯ {\displaystyle {\overline {A}}={\overline {U}}} P ¯ {\displaystyle {\overline {P}}} . This decomposition always exists; and so long as A is invertible, it is unique, with P positive-definite. Note that det A = det

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