Approximate Procedures

library(PoissonBinomial)

Poisson Approximation

The Poisson Approximation (DC) approach is requested with method = "Poisson". It is based on a Poisson distribution, whose parameter is the sum of the probabilities of success.

set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)

dpbinom(NULL, pp, wt, "Poisson")
#>  [1] 2.263593e-16 8.154460e-15 1.468798e-13 1.763753e-12 1.588454e-11
#>  [6] 1.144462e-10 6.871428e-10 3.536273e-09 1.592402e-08 6.373926e-08
#> [11] 2.296169e-07 7.519830e-07 2.257479e-06 6.255718e-06 1.609704e-05
#> [16] 3.865908e-05 8.704191e-05 1.844490e-04 3.691482e-04 6.999128e-04
#> [21] 1.260697e-03 2.162661e-03 3.541299e-03 5.546660e-03 8.325631e-03
#> [26] 1.199704e-02 1.662255e-02 2.217842e-02 2.853445e-02 3.544609e-02
#> [31] 4.256414e-02 4.946284e-02 5.568342e-02 6.078674e-02 6.440607e-02
#> [36] 6.629115e-02 6.633610e-02 6.458699e-02 6.122916e-02 5.655755e-02
#> [41] 5.093630e-02 4.475488e-02 3.838734e-02 3.216003e-02 2.633059e-02
#> [46] 2.107875e-02 1.650760e-02 1.265269e-02 9.495953e-03 6.981348e-03
#> [51] 5.029979e-03 3.552981e-03 2.461424e-03 1.673044e-03 1.116119e-03
#> [56] 7.310458e-04 4.702766e-04 2.972182e-04 1.846053e-04 1.127169e-04
#> [61] 6.767601e-05 3.996702e-05
ppbinom(NULL, pp, wt, "Poisson")
#>  [1] 2.263593e-16 8.380820e-15 1.552606e-13 1.919013e-12 1.780355e-11
#>  [6] 1.322498e-10 8.193925e-10 4.355666e-09 2.027968e-08 8.401894e-08
#> [11] 3.136359e-07 1.065619e-06 3.323097e-06 9.578815e-06 2.567585e-05
#> [16] 6.433494e-05 1.513768e-04 3.358259e-04 7.049740e-04 1.404887e-03
#> [21] 2.665584e-03 4.828245e-03 8.369543e-03 1.391620e-02 2.224184e-02
#> [26] 3.423887e-02 5.086142e-02 7.303984e-02 1.015743e-01 1.370204e-01
#> [31] 1.795845e-01 2.290474e-01 2.847308e-01 3.455175e-01 4.099236e-01
#> [36] 4.762147e-01 5.425508e-01 6.071378e-01 6.683670e-01 7.249245e-01
#> [41] 7.758608e-01 8.206157e-01 8.590031e-01 8.911631e-01 9.174937e-01
#> [46] 9.385724e-01 9.550800e-01 9.677327e-01 9.772287e-01 9.842100e-01
#> [51] 9.892400e-01 9.927930e-01 9.952544e-01 9.969275e-01 9.980436e-01
#> [56] 9.987746e-01 9.992449e-01 9.995421e-01 9.997267e-01 9.998394e-01
#> [61] 9.999071e-01 9.999471e-01

A comparison with exact computation shows that the approximation quality of the PA procedure increases with smaller probabilities of success. The reason is that the Poisson Binomial distribution approaches a Poisson distribution when the probabilities are very small.

set.seed(1)

# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "Poisson")
#>  [1] 0.0000150619 0.0001672374 0.0009284471 0.0034362888 0.0095385726
#>  [6] 0.0211820073 0.0391985129 0.0621763578 0.0862956727 0.1064633767
#> [11] 0.1182099310 0.1193204840 0.1104046811 0.0942969970 0.0747865595
#> [16] 0.0553587178 0.0384166744 0.0250913815 0.0154776776 0.0090449448
#> [21] 0.0050214559
dpbinom(NULL, pp)
#>  [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#>  [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "Poisson") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -9.555e-02  1.506e-05  9.038e-03 -2.461e-04  2.407e-02  4.379e-02

# U(0, 0.01) random probabilities of success
pp <- runif(20, 0, 0.01)
dpbinom(NULL, pp, method = "Poisson")
#>  [1]  9.095763e-01  8.620639e-02  4.085167e-03  1.290592e-04  3.057942e-06
#>  [6]  5.796418e-08  9.156063e-10  1.239697e-11  1.468825e-13  1.443290e-15
#> [11]  0.000000e+00  0.000000e+00  0.000000e+00 -1.110223e-16  0.000000e+00
#> [16]  1.110223e-16 -1.110223e-16  1.110223e-16 -1.110223e-16  1.110223e-16
#> [21]  0.000000e+00
dpbinom(NULL, pp)
#>  [1] 9.093051e-01 8.672423e-02 3.861917e-03 1.066765e-04 2.048094e-06
#>  [6] 2.902198e-08 3.145829e-10 2.667571e-12 1.794592e-14 9.656258e-17
#> [11] 4.170114e-19 1.444465e-21 3.994453e-24 8.738444e-27 1.490372e-29
#> [16] 1.938487e-32 1.859939e-35 1.249654e-38 5.381374e-42 1.245845e-45
#> [21] 9.511846e-50
summary(dpbinom(NULL, pp, method = "Poisson") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -5.178e-04  0.000e+00  0.000e+00  0.000e+00  6.000e-10  2.712e-04

Arithmetic Mean Binomial Approximation

The Arithmetic Mean Binomial Approximation (AMBA) approach is requested with method = "Mean". It is based on a Binomial distribution, whose parameter is the arithmetic mean of the probabilities of success.

set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
mean(rep(pp, wt))
#> [1] 0.5905641

dpbinom(NULL, pp, wt, "Mean")
#>  [1] 2.204668e-24 1.939788e-22 8.393759e-21 2.381049e-19 4.979863e-18
#>  [6] 8.188480e-17 1.102354e-15 1.249300e-14 1.216331e-13 1.033156e-12
#> [11] 7.749086e-12 5.182139e-11 3.114432e-10 1.693217e-09 8.373498e-09
#> [16] 3.784379e-08 1.569327e-07 5.991812e-07 2.112610e-06 6.896287e-06
#> [21] 2.088890e-05 5.882491e-05 1.542694e-04 3.773093e-04 8.616897e-04
#> [26] 1.839474e-03 3.673702e-03 6.868933e-03 1.203071e-02 1.974641e-02
#> [31] 3.038072e-02 4.382068e-02 5.925587e-02 7.510979e-02 8.921887e-02
#> [36] 9.927353e-02 1.034154e-01 1.007871e-01 9.181496e-02 7.810121e-02
#> [41] 6.195859e-02 4.577391e-02 3.143980e-02 2.003761e-02 1.182352e-02
#> [46] 6.442647e-03 3.232269e-03 1.487928e-03 6.259647e-04 2.395401e-04
#> [51] 8.292214e-05 2.579729e-05 7.155695e-06 1.752667e-06 3.745215e-07
#> [56] 6.875325e-08 1.062521e-08 1.344354e-09 1.337294e-10 9.807932e-12
#> [61] 4.716227e-13 1.110223e-14
ppbinom(NULL, pp, wt, "Mean")
#>  [1] 2.204668e-24 1.961834e-22 8.589942e-21 2.466948e-19 5.226557e-18
#>  [6] 8.711136e-17 1.189465e-15 1.368247e-14 1.353155e-13 1.168472e-12
#> [11] 8.917558e-12 6.073895e-11 3.721822e-10 2.065399e-09 1.043890e-08
#> [16] 4.828268e-08 2.052154e-07 8.043966e-07 2.917007e-06 9.813294e-06
#> [21] 3.070220e-05 8.952711e-05 2.437965e-04 6.211058e-04 1.482796e-03
#> [26] 3.322270e-03 6.995972e-03 1.386490e-02 2.589561e-02 4.564203e-02
#> [31] 7.602274e-02 1.198434e-01 1.790993e-01 2.542091e-01 3.434279e-01
#> [36] 4.427015e-01 5.461169e-01 6.469040e-01 7.387189e-01 8.168201e-01
#> [41] 8.787787e-01 9.245526e-01 9.559924e-01 9.760300e-01 9.878536e-01
#> [46] 9.942962e-01 9.975285e-01 9.990164e-01 9.996424e-01 9.998819e-01
#> [51] 9.999648e-01 9.999906e-01 9.999978e-01 9.999995e-01 9.999999e-01
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00

A comparison with exact computation shows that the approximation quality of the AMBA procedure increases when the probabilities of success are closer to each other. The reason is that, although the expectation remains unchanged, the distribution’s variance becomes smaller the less the probabilities differ. Since this variance is minimized by equal probabilities (but still underestimated), the AMBA method is best suited for situations with very similar probabilities of success.

set.seed(1)

# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "Mean")
#>  [1] 9.203176e-08 2.297178e-06 2.723611e-05 2.039497e-04 1.081780e-03
#>  [6] 4.320318e-03 1.347977e-02 3.364646e-02 6.823695e-02 1.135495e-01
#> [11] 1.558851e-01 1.768638e-01 1.655492e-01 1.271454e-01 7.934094e-02
#> [16] 3.960811e-02 1.544760e-02 4.536271e-03 9.435709e-04 1.239589e-04
#> [21] 7.735255e-06
dpbinom(NULL, pp)
#>  [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#>  [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "Mean") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -3.801e-02  2.290e-06  6.360e-04  0.000e+00  8.837e-03  1.662e-02

# U(0.3, 0.5) random probabilities of success
pp <- runif(20, 0.3, 0.5)
dpbinom(NULL, pp, method = "Mean")
#>  [1] 4.348271e-05 5.672598e-04 3.515127e-03 1.375712e-02 3.813748e-02
#>  [6] 7.960444e-02 1.298114e-01 1.693472e-01 1.795010e-01 1.561137e-01
#> [11] 1.120132e-01 6.642197e-02 3.249439e-02 1.304339e-02 4.253984e-03
#> [16] 1.109919e-03 2.262438e-04 3.472347e-05 3.774915e-06 2.591904e-07
#> [21] 8.453263e-09
dpbinom(NULL, pp)
#>  [1] 4.015121e-05 5.344728e-04 3.370391e-03 1.338738e-02 3.756479e-02
#>  [6] 7.915145e-02 1.299445e-01 1.702071e-01 1.806555e-01 1.569062e-01
#> [11] 1.121277e-01 6.604356e-02 3.200604e-02 1.269255e-02 4.078679e-03
#> [16] 1.045709e-03 2.088926e-04 3.133484e-05 3.320483e-06 2.216332e-07
#> [21] 7.008006e-09
summary(dpbinom(NULL, pp, method = "Mean") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -1.155e-03  1.400e-09  1.735e-05  0.000e+00  3.508e-04  5.727e-04

# U(0.39, 0.41) random probabilities of success
pp <- runif(20, 0.39, 0.41)
dpbinom(NULL, pp, method = "Mean")
#>  [1] 3.638616e-05 4.854405e-04 3.076305e-03 1.231262e-02 3.490673e-02
#>  [6] 7.451247e-02 1.242621e-01 1.657824e-01 1.797056e-01 1.598344e-01
#> [11] 1.172824e-01 7.112295e-02 3.558286e-02 1.460687e-02 4.871885e-03
#> [16] 1.299951e-03 2.709859e-04 4.253314e-05 4.728746e-06 3.320414e-07
#> [21] 1.107470e-08
dpbinom(NULL, pp)
#>  [1] 3.636149e-05 4.851935e-04 3.075192e-03 1.230970e-02 3.490204e-02
#>  [6] 7.450845e-02 1.242626e-01 1.657891e-01 1.797153e-01 1.598415e-01
#> [11] 1.172840e-01 7.112011e-02 3.557873e-02 1.460374e-02 4.870251e-03
#> [16] 1.299328e-03 2.708111e-04 4.249771e-05 4.723809e-06 3.316172e-07
#> [21] 1.105772e-08
summary(dpbinom(NULL, pp, method = "Mean") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -9.641e-06  1.700e-11  1.747e-07  0.000e+00  2.844e-06  4.689e-06

Geometric Mean Binomial Approximation - Variant A

The Geometric Mean Binomial Approximation (Variant A) (GMBA-A) approach is requested with method = "GeoMean". It is based on a Binomial distribution, whose parameter is the geometric mean of the probabilities of success: \[\hat{p} = \sqrt[n]{p_1 \cdot ... \cdot p_n}\]

set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
prod(rep(pp, wt))^(1/sum(wt))
#> [1] 0.4669916

dpbinom(NULL, pp, wt, "GeoMean")
#>  [1] 2.141782e-17 1.144670e-15 3.008684e-14 5.184208e-13 6.586057e-12
#>  [6] 6.578175e-11 5.379195e-10 3.703028e-09 2.189958e-08 1.129911e-07
#> [11] 5.147813e-07 2.091103e-06 7.633772e-06 2.520966e-05 7.572779e-05
#> [16] 2.078916e-04 5.236606e-04 1.214475e-03 2.601021e-03 5.157435e-03
#> [21] 9.489168e-03 1.623184e-02 2.585712e-02 3.841422e-02 5.328923e-02
#> [26] 6.909972e-02 8.382634e-02 9.520502e-02 1.012875e-01 1.009827e-01
#> [31] 9.437363e-02 8.268481e-02 6.791600e-02 5.229152e-02 3.772988e-02
#> [36] 2.550094e-02 1.613623e-02 9.552467e-03 5.285892e-03 2.731219e-03
#> [41] 1.316117e-03 5.906156e-04 2.464113e-04 9.539397e-05 3.419132e-05
#> [46] 1.131690e-05 3.448772e-06 9.643463e-07 2.464308e-07 5.728188e-08
#> [51] 1.204491e-08 2.276152e-09 3.835067e-10 5.705769e-11 7.406076e-12
#> [56] 8.257839e-13 7.760459e-14 5.884182e-15 4.440892e-16 0.000000e+00
#> [61] 0.000000e+00 0.000000e+00
ppbinom(NULL, pp, wt, "GeoMean")
#>  [1] 2.141782e-17 1.166088e-15 3.125293e-14 5.496737e-13 7.135731e-12
#>  [6] 7.291748e-11 6.108370e-10 4.313865e-09 2.621345e-08 1.392046e-07
#> [11] 6.539859e-07 2.745088e-06 1.037886e-05 3.558852e-05 1.113163e-04
#> [16] 3.192079e-04 8.428685e-04 2.057343e-03 4.658364e-03 9.815799e-03
#> [21] 1.930497e-02 3.553681e-02 6.139393e-02 9.980815e-02 1.530974e-01
#> [26] 2.221971e-01 3.060234e-01 4.012285e-01 5.025160e-01 6.034986e-01
#> [31] 6.978723e-01 7.805571e-01 8.484731e-01 9.007646e-01 9.384945e-01
#> [36] 9.639954e-01 9.801316e-01 9.896841e-01 9.949700e-01 9.977012e-01
#> [41] 9.990173e-01 9.996080e-01 9.998544e-01 9.999498e-01 9.999840e-01
#> [46] 9.999953e-01 9.999987e-01 9.999997e-01 9.999999e-01 1.000000e+00
#> [51] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00

It is known that the geometric mean of the probabilities of success is always smaller than their arithmetic mean. Thus, we get a stochastically smaller binomial distribution. A comparison with exact computation shows that the approximation quality of the GMBA-A procedure increases when the probabilities of success are closer to each other:

set.seed(1)

# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "GeoMean")
#>  [1] 4.557123e-06 7.742984e-05 6.249130e-04 3.185359e-03 1.150098e-02
#>  [6] 3.126602e-02 6.640491e-02 1.128282e-01 1.557610e-01 1.764351e-01
#> [11] 1.648790e-01 1.273387e-01 8.113517e-02 4.241734e-02 1.801777e-02
#> [16] 6.122779e-03 1.625497e-03 3.249263e-04 4.600672e-05 4.114199e-06
#> [21] 1.747603e-07
dpbinom(NULL, pp)
#>  [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#>  [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "GeoMean") - dpbinom(NULL, pp))
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#> -0.11151 -0.01493  0.00000  0.00000  0.01140  0.10279

# U(0.4, 0.6) random probabilities of success
pp <- runif(20, 0.4, 0.6)
dpbinom(NULL, pp, method = "GeoMean")
#>  [1] 1.317886e-06 2.551200e-05 2.345875e-04 1.362363e-03 5.604265e-03
#>  [6] 1.735823e-02 4.200318e-02 8.131092e-02 1.278907e-01 1.650496e-01
#> [11] 1.757292e-01 1.546280e-01 1.122499e-01 6.686047e-02 3.235759e-02
#> [16] 1.252775e-02 3.789307e-03 8.629936e-04 1.392173e-04 1.418425e-05
#> [21] 6.864565e-07
dpbinom(NULL, pp)
#>  [1] 1.046635e-06 2.098187e-05 1.993006e-04 1.192678e-03 5.043114e-03
#>  [6] 1.601621e-02 3.964022e-02 7.829406e-02 1.253351e-01 1.642218e-01
#> [11] 1.770816e-01 1.574210e-01 1.151700e-01 6.896627e-02 3.347297e-02
#> [16] 1.296524e-02 3.913788e-03 8.873960e-04 1.421738e-04 1.435144e-05
#> [21] 6.864565e-07
summary(dpbinom(NULL, pp, method = "GeoMean") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -0.0029201 -0.0004375  0.0000000  0.0000000  0.0005612  0.0030169

# U(0.49, 0.51) random probabilities of success
pp <- runif(20, 0.49, 0.51)
dpbinom(NULL, pp, method = "GeoMean")
#>  [1] 9.491177e-07 1.899145e-05 1.805052e-04 1.083550e-03 4.607292e-03
#>  [6] 1.475040e-02 3.689366e-02 7.382266e-02 1.200193e-01 1.601024e-01
#> [11] 1.761970e-01 1.602558e-01 1.202494e-01 7.403508e-02 3.703527e-02
#> [16] 1.482120e-02 4.633845e-03 1.090839e-03 1.818935e-04 1.915586e-05
#> [21] 9.582517e-07
dpbinom(NULL, pp)
#>  [1] 9.472606e-07 1.895984e-05 1.802539e-04 1.082315e-03 4.603107e-03
#>  [6] 1.474011e-02 3.687497e-02 7.379784e-02 1.199969e-01 1.600932e-01
#> [11] 1.762060e-01 1.602781e-01 1.202742e-01 7.405383e-02 3.704562e-02
#> [16] 1.482542e-02 4.635093e-03 1.091093e-03 1.819256e-04 1.915775e-05
#> [21] 9.582517e-07
summary(dpbinom(NULL, pp, method = "GeoMean") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -2.485e-05 -4.219e-06  0.000e+00  0.000e+00  4.185e-06  2.482e-05

Geometric Mean Binomial Approximation - Variant B

The Geometric Mean Binomial Approximation (Variant B) (GMBA-B) approach is requested with method = "GeoMeanCounter". It is based on a Binomial distribution, whose parameter is 1 minus the geometric mean of the probabilities of failure: \[\hat{p} = 1 - \sqrt[n]{(1 - p_1) \cdot ... \cdot (1 - p_n)}\]

set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
1 - prod(1 - rep(pp, wt))^(1/sum(wt))
#> [1] 0.7275426

dpbinom(NULL, pp, wt, "GeoMeanCounter")
#>  [1] 3.574462e-35 5.822379e-33 4.664248e-31 2.449471e-29 9.484189e-28
#>  [6] 2.887121e-26 7.195512e-25 1.509685e-23 2.721134e-22 4.279009e-21
#> [11] 5.941642e-20 7.356037e-19 8.184508e-18 8.237686e-17 7.541858e-16
#> [16] 6.310225e-15 4.844429e-14 3.424255e-13 2.235148e-12 1.350769e-11
#> [21] 7.574609e-11 3.948978e-10 1.917264e-09 8.681177e-09 3.670379e-08
#> [26] 1.450549e-07 5.363170e-07 1.856461e-06 6.019586e-06 1.829121e-05
#> [31] 5.209921e-05 1.391205e-04 3.482749e-04 8.172712e-04 1.797236e-03
#> [36] 3.702208e-03 7.139892e-03 1.288219e-02 2.172588e-02 3.421374e-02
#> [41] 5.024851e-02 6.872559e-02 8.738947e-02 1.031108e-01 1.126377e-01
#> [46] 1.136267e-01 1.055364e-01 8.994057e-02 7.004907e-02 4.962603e-02
#> [51] 3.180393e-02 1.831737e-02 9.406320e-03 4.265268e-03 1.687339e-03
#> [56] 5.734528e-04 1.640669e-04 3.843049e-05 7.077304e-06 9.609416e-07
#> [61] 8.553338e-08 3.744258e-09
ppbinom(NULL, pp, wt, "GeoMeanCounter")
#>  [1] 3.574462e-35 5.858123e-33 4.722829e-31 2.496699e-29 9.733859e-28
#>  [6] 2.984460e-26 7.493958e-25 1.584624e-23 2.879597e-22 4.566969e-21
#> [11] 6.398339e-20 7.995871e-19 8.984095e-18 9.136095e-17 8.455467e-16
#> [16] 7.155772e-15 5.560007e-14 3.980256e-13 2.633173e-12 1.614086e-11
#> [21] 9.188695e-11 4.867847e-10 2.404049e-09 1.108523e-08 4.778901e-08
#> [26] 1.928440e-07 7.291610e-07 2.585622e-06 8.605207e-06 2.689642e-05
#> [31] 7.899562e-05 2.181161e-04 5.663910e-04 1.383662e-03 3.180899e-03
#> [36] 6.883107e-03 1.402300e-02 2.690519e-02 4.863107e-02 8.284481e-02
#> [41] 1.330933e-01 2.018189e-01 2.892084e-01 3.923192e-01 5.049569e-01
#> [46] 6.185836e-01 7.241200e-01 8.140606e-01 8.841097e-01 9.337357e-01
#> [51] 9.655396e-01 9.838570e-01 9.932633e-01 9.975286e-01 9.992159e-01
#> [56] 9.997894e-01 9.999534e-01 9.999919e-01 9.999989e-01 9.999999e-01
#> [61] 1.000000e+00 1.000000e+00

It is known that the geometric mean of the probabilities of failure is always smaller than their arithmetic mean. As a result, 1 minus the geometric mean is larger than 1 minus the arithmetic mean. Thus, we get a stochastically larger binomial distribution. A comparison with exact computation shows that the approximation quality of the GMBA-B procedure again increases when the probabilities of success are closer to each other:

set.seed(1)

# U(0, 1) random probabilities of success
pp <- runif(20)
dpbinom(NULL, pp, method = "GeoMeanCounter")
#>  [1] 4.401037e-11 2.019854e-09 4.403304e-08 6.062685e-07 5.912743e-06
#>  [6] 4.341843e-05 2.490859e-04 1.143179e-03 4.262876e-03 1.304297e-02
#> [11] 3.292337e-02 6.868258e-02 1.182069e-01 1.669263e-01 1.915269e-01
#> [16] 1.758024e-01 1.260695e-01 6.807004e-02 2.603394e-02 6.288561e-03
#> [21] 7.215333e-04
dpbinom(NULL, pp)
#>  [1] 4.401037e-11 7.873212e-09 3.624610e-07 7.952504e-06 1.014602e-04
#>  [6] 8.311558e-04 4.642470e-03 1.838525e-02 5.297347e-02 1.129135e-01
#> [11] 1.798080e-01 2.148719e-01 1.926468e-01 1.289706e-01 6.384266e-02
#> [16] 2.299142e-02 5.871700e-03 1.021142e-03 1.129421e-04 6.977021e-06
#> [21] 1.747603e-07
summary(dpbinom(NULL, pp, method = "GeoMeanCounter") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -1.469e-01 -1.724e-02 -3.200e-07  0.000e+00  2.592e-02  1.528e-01

# U(0.4, 0.6) random probabilities of success
pp <- runif(20, 0.4, 0.6)
dpbinom(NULL, pp, method = "GeoMeanCounter")
#>  [1] 1.046635e-06 2.073844e-05 1.951870e-04 1.160254e-03 4.885321e-03
#>  [6] 1.548796e-02 3.836059e-02 7.600922e-02 1.223688e-01 1.616443e-01
#> [11] 1.761588e-01 1.586582e-01 1.178895e-01 7.187414e-02 3.560358e-02
#> [16] 1.410928e-02 4.368234e-03 1.018282e-03 1.681387e-04 1.753458e-05
#> [21] 8.685930e-07
dpbinom(NULL, pp)
#>  [1] 1.046635e-06 2.098187e-05 1.993006e-04 1.192678e-03 5.043114e-03
#>  [6] 1.601621e-02 3.964022e-02 7.829406e-02 1.253351e-01 1.642218e-01
#> [11] 1.770816e-01 1.574210e-01 1.151700e-01 6.896627e-02 3.347297e-02
#> [16] 1.296524e-02 3.913788e-03 8.873960e-04 1.421738e-04 1.435144e-05
#> [21] 6.864565e-07
summary(dpbinom(NULL, pp, method = "GeoMeanCounter") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -0.0029663 -0.0005283  0.0000000  0.0000000  0.0004544  0.0029079

# U(0.49, 0.51) random probabilities of success
pp <- runif(20, 0.49, 0.51)
dpbinom(NULL, pp, method = "GeoMeanCounter")
#>  [1] 9.472606e-07 1.895800e-05 1.802225e-04 1.082065e-03 4.601880e-03
#>  [6] 1.473596e-02 3.686475e-02 7.377926e-02 1.199722e-01 1.600709e-01
#> [11] 1.761969e-01 1.602871e-01 1.202964e-01 7.407854e-02 3.706427e-02
#> [16] 1.483571e-02 4.639289e-03 1.092334e-03 1.821786e-04 1.918963e-05
#> [21] 9.601293e-07
dpbinom(NULL, pp)
#>  [1] 9.472606e-07 1.895984e-05 1.802539e-04 1.082315e-03 4.603107e-03
#>  [6] 1.474011e-02 3.687497e-02 7.379784e-02 1.199969e-01 1.600932e-01
#> [11] 1.762060e-01 1.602781e-01 1.202742e-01 7.405383e-02 3.704562e-02
#> [16] 1.482542e-02 4.635093e-03 1.091093e-03 1.819256e-04 1.915775e-05
#> [21] 9.582517e-07
summary(dpbinom(NULL, pp, method = "GeoMeanCounter") - dpbinom(NULL, pp))
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -2.467e-05 -4.159e-06  0.000e+00  0.000e+00  4.196e-06  2.470e-05

Normal Approximation

The Normal Approximation (NA) approach is requested with method = "Normal". It is based on a Normal distribution, whose parameters are derived from the theoretical mean and variance of the input probabilities of success.

set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
mean(rep(pp, wt))
#> [1] 0.5905641

dpbinom(NULL, pp, wt, "Normal")
#>  [1] 2.552770e-32 1.207834e-30 5.219650e-29 2.022022e-27 7.021785e-26
#>  [6] 2.185917e-24 6.100302e-23 1.526188e-21 3.423032e-20 6.882841e-19
#> [11] 1.240755e-17 2.005270e-16 2.905604e-15 3.774712e-14 4.396661e-13
#> [16] 4.591569e-12 4.299381e-11 3.609645e-10 2.717342e-09 1.834224e-08
#> [21] 1.110185e-07 6.025326e-07 2.932337e-06 1.279682e-05 5.007841e-05
#> [26] 1.757379e-04 5.530339e-04 1.560683e-03 3.949650e-03 8.963710e-03
#> [31] 1.824341e-02 3.329786e-02 5.450317e-02 8.000636e-02 1.053238e-01
#> [36] 1.243451e-01 1.316535e-01 1.250080e-01 1.064497e-01 8.129267e-02
#> [41] 5.567468e-02 3.419491e-02 1.883477e-02 9.303614e-03 4.121280e-03
#> [46] 1.637186e-03 5.832371e-04 1.863241e-04 5.337829e-05 1.371282e-05
#> [51] 3.159002e-06 6.525712e-07 1.208800e-07 2.007813e-08 2.990389e-09
#> [56] 3.993563e-10 4.782064e-11 5.134337e-12 4.942713e-13 4.263256e-14
#> [61] 3.330669e-15 2.220446e-16
ppbinom(NULL, pp, wt, "Normal")
#>  [1] 2.552770e-32 1.233362e-30 5.342987e-29 2.075452e-27 7.229330e-26
#>  [6] 2.258210e-24 6.326123e-23 1.589449e-21 3.581977e-20 7.241039e-19
#> [11] 1.313165e-17 2.136587e-16 3.119262e-15 4.086639e-14 4.805325e-13
#> [16] 5.072102e-12 4.806591e-11 4.090305e-10 3.126373e-09 2.146861e-08
#> [21] 1.324871e-07 7.350197e-07 3.667357e-06 1.646417e-05 6.654258e-05
#> [26] 2.422805e-04 7.953144e-04 2.355997e-03 6.305647e-03 1.526936e-02
#> [31] 3.351276e-02 6.681062e-02 1.213138e-01 2.013201e-01 3.066439e-01
#> [36] 4.309891e-01 5.626426e-01 6.876506e-01 7.941003e-01 8.753930e-01
#> [41] 9.310676e-01 9.652625e-01 9.840973e-01 9.934009e-01 9.975222e-01
#> [46] 9.991594e-01 9.997426e-01 9.999290e-01 9.999823e-01 9.999960e-01
#> [51] 9.999992e-01 9.999999e-01 1.000000e+00 1.000000e+00 1.000000e+00
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00

A comparison with exact computation shows that the approximation quality of the NA procedure increases with larger numbers of probabilities of success:

set.seed(1)

# 10 random probabilities of success
pp <- runif(10)
dpn <- dpbinom(NULL, pp, method = "Normal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -5.331e-03 -1.042e-03  4.912e-04 -3.269e-06  1.658e-03  2.655e-03

# 1000 random probabilities of success
pp <- runif(1000)
dpn <- dpbinom(NULL, pp, method = "Normal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -8.412e-06  0.000e+00  2.656e-09  0.000e+00  6.073e-07  3.815e-06

# 100000 random probabilities of success
pp <- runif(100000)
dpn <- dpbinom(NULL, pp, method = "Normal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -4.484e-09  0.000e+00  8.990e-13  0.000e+00  4.919e-10  2.734e-09

Refined Normal Approximation

The Refined Normal Approximation (RNA) approach is requested with method = "RefinedNormal". It is based on a Normal distribution, whose parameters are derived from the theoretical mean, variance and skewness of the input probabilities of success.

set.seed(1)
pp <- runif(10)
wt <- sample(1:10, 10, TRUE)
mean(rep(pp, wt))
#> [1] 0.5905641

dpbinom(NULL, pp, wt, "RefinedNormal")
#>  [1] 2.579548e-31 1.128297e-29 4.507210e-28 1.611452e-26 5.156486e-25
#>  [6] 1.476806e-23 3.785627e-22 8.685911e-21 1.783953e-19 3.280039e-18
#> [11] 5.399492e-17 7.959230e-16 1.050796e-14 1.242802e-13 1.317210e-12
#> [16] 1.251531e-11 1.066498e-10 8.155390e-10 5.599786e-09 3.455053e-08
#> [21] 1.917106e-07 9.574753e-07 4.308224e-06 1.748069e-05 6.401569e-05
#> [26] 2.117447e-04 6.329842e-04 1.710740e-03 4.180480e-03 9.234968e-03
#> [31] 1.843341e-02 3.322175e-02 5.401115e-02 7.912655e-02 1.043358e-01
#> [36] 1.236782e-01 1.316360e-01 1.256489e-01 1.074322e-01 8.218619e-02
#> [41] 5.618825e-02 3.428872e-02 1.865323e-02 9.032795e-03 3.886960e-03
#> [46] 1.483178e-03 5.004545e-04 1.487517e-04 3.873113e-05 8.757189e-06
#> [51] 1.693868e-06 2.722346e-07 3.388544e-08 2.218356e-09 0.000000e+00
#> [56] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> [61] 0.000000e+00 0.000000e+00
ppbinom(NULL, pp, wt, "RefinedNormal")
#>  [1] 2.579548e-31 1.154092e-29 4.622620e-28 1.657678e-26 5.322254e-25
#>  [6] 1.530028e-23 3.938629e-22 9.079774e-21 1.874750e-19 3.467514e-18
#> [11] 5.746244e-17 8.533855e-16 1.136134e-14 1.356415e-13 1.452852e-12
#> [16] 1.396817e-11 1.206179e-10 9.361569e-10 6.535943e-09 4.108647e-08
#> [21] 2.327971e-07 1.190272e-06 5.498496e-06 2.297918e-05 8.699487e-05
#> [26] 2.987396e-04 9.317238e-04 2.642463e-03 6.822944e-03 1.605791e-02
#> [31] 3.449132e-02 6.771307e-02 1.217242e-01 2.008508e-01 3.051866e-01
#> [36] 4.288648e-01 5.605008e-01 6.861497e-01 7.935820e-01 8.757682e-01
#> [41] 9.319564e-01 9.662451e-01 9.848984e-01 9.939312e-01 9.978181e-01
#> [46] 9.993013e-01 9.998018e-01 9.999505e-01 9.999892e-01 9.999980e-01
#> [51] 9.999997e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [56] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
#> [61] 1.000000e+00 1.000000e+00

A comparison with exact computation shows that the approximation quality of the RNA procedure increases with larger numbers of probabilities of success:

set.seed(1)

# 10 random probabilities of success
pp <- runif(10)
dpn <- dpbinom(NULL, pp, method = "RefinedNormal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -3.954e-03 -6.920e-04  3.543e-04 -2.294e-06  1.717e-03  2.360e-03

# 1000 random probabilities of success
pp <- runif(1000)
dpn <- dpbinom(NULL, pp, method = "RefinedNormal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -2.974e-06  0.000e+00  3.181e-10  0.000e+00  3.747e-07  2.270e-06

# 100000 random probabilities of success
pp <- runif(100000)
dpn <- dpbinom(NULL, pp, method = "RefinedNormal")
dpd <- dpbinom(NULL, pp)
idx <- which(dpn != 0 & dpd != 0)
summary((dpn - dpd)[idx])
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -3.126e-09  0.000e+00  6.337e-13  0.000e+00  4.632e-10  2.293e-09

Performance Comparisons

To assess the performance of the approximation procedures, we use the microbenchmark package. Each algorithm has to calculate the PMF repeatedly based on random probability vectors. The run times are then summarized in a table that presents, among other statistics, their minima, maxima and means. The following results were recorded on an AMD Ryzen 7 1800X with 32 GiB of RAM and Ubuntu 18.04.3 (running inside a VirtualBox VM; the host system is Windows 10 Education).

library(microbenchmark)
set.seed(1)

f1 <- function() dpbinom(NULL, runif(4000), method = "Normal")
f2 <- function() dpbinom(NULL, runif(4000), method = "RefinedNormal")
f3 <- function() dpbinom(NULL, runif(4000), method = "Poisson")
f4 <- function() dpbinom(NULL, runif(4000), method = "Mean")
f5 <- function() dpbinom(NULL, runif(4000), method = "GeoMean")
f6 <- function() dpbinom(NULL, runif(4000), method = "GeoMeanCounter")
f7 <- function() dpbinom(NULL, runif(4000), method = "DivideFFT")

microbenchmark(f1(), f2(), f3(), f4(), f5(), f6(), f7())
#> Unit: microseconds
#>  expr      min        lq      mean   median        uq       max neval
#>  f1()  513.363  544.0810  621.0367  554.415  585.3525  4636.997   100
#>  f2()  669.025  706.0895  775.2231  728.090  755.5425  4116.651   100
#>  f3() 1135.169 1173.4160 1254.6436 1188.981 1220.1285  5616.675   100
#>  f4() 1182.978 1219.7075 1346.1717 1245.541 1271.1640  5226.142   100
#>  f5() 1260.504 1333.6520 1407.2786 1352.171 1392.0160  4438.365   100
#>  f6() 1282.064 1336.0660 1413.6936 1356.259 1386.6005  5288.800   100
#>  f7() 5422.841 5789.1185 6825.5557 5952.048 6492.4720 13835.039   100

Clearly, the NA procedure is the fastest, followed by the RNA method, which needs roughly 30-40% more time, and the PA, AMBA and GMBA approaches that need almost twice as long as the NA algorithm. AMBA, GMBA-A and GMBA-B procedures exhibit almost equal mean execution speed, with the AMBA algorithm being slightly faster. All of the approximation procedures outperform the fastest exact approach, DC-FFT, by far. Even the slowest approximate algorithm is around 4x as fast as DC-FFT.