Skip to main content
. 2018 May 23;20(6):396. doi: 10.3390/e20060396
a Hyperparameter of prior on probability vector π
b Hyperparameter of prior on probability vector π
Cj Number of categories of the jth predictor
Ci ith class of dynamical systems
Dy Number of time-lags of variable y
Dθ Number of time-lags of variable θ
k˜j Number of clusters formed by xj
kj Dimension of the jth mixture probability vector
k Vector {kj}j=1q
L Number of truncations in a Pitman-Yor process
N Number of iterations in Algorithm 1
q Number of predictors
s Realization of a latent allocation-class variable
T Number of pairs of variables and predictors
xj,t jth latent allocation-class variables at time t
xj jth latent allocation-class variables {xj,t}t=1T
xt Latent allocation-class variables {xj,t}j=1q at time t
x Latent allocation-class variables {xt}t=1T
yt Variable y at time t
y Variables {yt}t=1T
zj,t jth predictor at time t
zj jth predictors {zj,t}t=1T
zt Predictors {zj,t}j=1q at time t
z Predictors {zt}t=1T
α Hyperparameter of prior on λ
βj Hyperparameter of prior on ωj
θt Variable θ at time t
Θ Threshold
λs1,,sq Probability vector {λs1,,sq(c)}c=1C0
Λ Set of predictors
λ˜ Conditional probability tensor {λs1,,sq}s1,,sq
λl Probability vector {λl(c)}c=1C0
λ Sequence {λl}l=1
μj Hyperparameter of prior on kj
π Probability vector {πl}l=1
ϕ Collection {ϕs1,,sq}s1,,sq
ψ(k) Time-invariant spatial variables for kth experiment
ω(j)(c) Mixture probability vector {ωs(j)(c)}s=1kj
ω(j) Mixture probability matrix {ωs(j)(c)}c=1Cj
ω Mixture probability tensor {ω(j)}j=1q
Pertinent Acronyms
BF Bayes Factor
Beta Beta Distribution
Dir Uniform Dirichlet Distribution
HOSVD Higher order singular value decomposition
Mult Multinomial Distribution
ROC Receiver operating characteristic