em_exp_emstep | R Documentation |
Execute one EM step for software reliability models.
em_exp_emstep(params, data)
em_gamma_emstep(params, data, divide = 15L, eps = 1e-10)
em_llogis_emstep(params, data)
em_llogis_estep(params, data)
em_llogis_pllf(params, data, w1)
em_lnorm_emstep(params, data)
em_lxvmax_estep(params, data)
em_lxvmax_pllf(params, data, w1)
em_lxvmin_estep(params, data)
em_lxvmin_pllf(params, data, w1)
em_pareto_emstep(params, data)
em_tlogis_emstep_mo(params, data)
em_tlogis_estep(params, data)
em_tlogis_pllf(params, data, w0, w1)
em_tnorm_emstep(params, data)
em_txvmax_emstep_mo(params, data)
em_txvmax_estep(params, data)
em_txvmax_pllf(params, data, w0, w1)
em_txvmin_estep(params, data)
em_txvmin_pllf(params, data, w0, w1)
params |
A numeric vector for model parameters |
data |
A faultdata |
divide |
An integer for the number of integration points. |
eps |
A numeric value for tolerance error. |
w1 |
A numeric value indicating the expected number of faults after te. |
w0 |
A numeric value indicating the expected number of faults before 0. |
... |
Other parameters |
em_tlogis_emstep_mo
is an emstep based on Marshall-Olkin-type (maximum) with Exp
em_tnorm_emstep
has been modified by using EM for truncated distribution. Concretely,
when the distribution is truncated at origin, the expected value is given by
\text{E}[h(X)|D] = \sum_{i=1}^k h(x_i) + \frac{k}{\overline{F}(0)} \int_{-\infty}^0 h(x) f(x) dx.
em_txvmax_emstep_mo
is an emstep based on Marshall-Olkin-type (maximum) with Exp
A list with an updated parameter vector (param), absolute difference of parameter vector (pdiff), log-likelihood function for a given parameter vector (llf), the number of total faults (total).
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