ea_mixG_DL_PT | R Documentation |
Simulate Langevin diffusion using the Exact Algorithm with pi = exp(-(beta*x^4)/2)
ea_mixG_DL_PT( x0, y, s, t, n_comp, weights, means, sds, beta, precondition, bounds_multiplier = 1.1, logarithm )
x0 |
start value |
y |
end value |
s |
start time |
t |
end time |
n_comp |
integer number of components of mixture Gaussian |
weights |
vector: weights of mixture Gaussian |
means |
vector: means of mixture Gaussian |
sds |
vector: st.devs of mixture Gaussian |
beta |
real value |
precondition |
precondition value (i.e the covariance for the Langevin diffusion) |
bounds_multiplier |
scalar value to multiply bounds by (should greater than or equal to 1) |
logarithm |
logical value to determine if log probability is returned (TRUE) or not (FALSE) |
weights <- c(0.4, 0.6) means <- c(-8, 15) sds <- c(1, 2) beta <- 1/4 precondition <- 1.5 x0 <- -9 y <- -8 s <- 0 t <- 1 # simulate event probability of diffusion from -9 to -8 between [0,1] ea_mixG_DL_PT(x0 = x0, y = y, s = s, t = t, n_comp = 2, weights = weights, means = means, sds = sds, beta = beta, precondition = precondition, bounds_multiplier = 1, logarithm = FALSE) # simulate event probability of diffusion from -9 to 15 between [0,1] x0 <- -9 y <- 15 ea_mixG_DL_PT(x0 = x0, y = y, s = s, t = t, n_comp = 2, weights = weights, means = means, sds = sds, beta = beta, precondition = precondition, bounds_multiplier = 1, logarithm = FALSE)
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