relibmss

A Python package for binary/multi state systems with BDD/MDD.

Installation

pip install relibmss

Usage

The recommended workflow is node-centric: build an expression with the overloaded operators, convert it to a decision diagram with getbdd (BSS) / getmdd (MSS), then call the analysis methods on the resulting node:

import relibmss as ms

bss = ms.BSS()
A, B, C = bss.defvar('A'), bss.defvar('B'), bss.defvar('C')
top = A & B | C                      # build an expression
node = bss.getbdd(top)               # convert to a BDD
print(node.prob({'A': 0.1, 'B': 0.2, 'C': 0.3}))

(A lower-level API — ms.BDD() / ms.MDD() / ms.ZDD() — lets you build directly on nodes without the expression layer; see Standalone (low-level) managers.)

Calculate the probability of a fault tree

import relibmss as ms

# Create a binary system (fault tree)
bss = ms.BSS()

# Define events (this version only supports repeated events)
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Make a tree (& is AND gate, | is OR gate)
top = A & B | C
node = bss.getbdd(top)

# Point probabilities
prob = {'A': 0.1, 'B': 0.2, 'C': 0.3}
print(node.prob(prob))

# Interval probabilities
probint = {'A': (0.1, 0.2), 'B': (0.2, 0.3), 'C': (0.3, 0.4)}
print(node.prob_interval(probint))

prob assumes the events are s-independent. The probability of each variable is multiplied along the paths of the diagram, so a dependence between two events is not captured by giving them marginal probabilities. See Dependent events (common-cause failures) for how to model dependence explicitly — the same applies to prob_interval and bmeas, and to the multi-state side.

Boolean operators

In addition to & (AND) and | (OR), events support ~ (NOT) and ^ (XOR).

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')

prob = {'A': 0.1, 'B': 0.2}

# NOT: `~A` and `bss.Not(A)` are equivalent
print(bss.getbdd(~A).prob(prob))         # 0.9
print(bss.getbdd(bss.Not(A)).prob(prob)) # 0.9

# XOR: exactly one of A and B occurs
print(bss.getbdd(A ^ B).prob(prob))      # 0.1*0.8 + 0.9*0.2 = 0.26

Variable order

The variable order determines the size of the BDD/MDD, so it can matter a lot for large models. defvar only declares a variable; the diagram variable itself is created when the expression is first converted (getbdd/getmdd), in order of first appearance in the expression. Variables that never appear are not created at all.

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Default: first appearance wins, not declaration order
bss.getbdd(C & A | B)
print(bss.get_varorder())   # ['C', 'A', 'B']

Use set_varorder to pin the order explicitly. It must be called before the first getbdd/getmdd, because the order is fixed once the variables are created (there is no dynamic reordering); calling it afterwards raises an error. Variables you leave out are still created on first appearance, after the ones you listed.

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

bss.set_varorder(['C', 'B', 'A'])
bss.getbdd(A & B | C)
print(bss.get_varorder())   # ['C', 'B', 'A']

Passing vars=[...] to the constructor does the same thing: ms.BSS(vars=['C', 'B', 'A']).

get_varorder also lets you carry an order over to another manager. Note the two differ, because MDD variables need their number of states:

bss.get_varorder()   # ['C', 'B', 'A']                 -- BSS/BDD: names
mss.get_varorder()   # [('C', 3), ('B', 3), ('A', 2)]  -- MSS/MDD: (name, states)

bdd = ms.BDD(bss.get_varorder())   # reuse the order in a raw BDD
mdd = ms.MDD(mss.get_varorder())   # likewise for an MDD

Obtain the minimal path / cut vectors

For a structure function φ, minpath() returns the prime implicants of φ and mincut() returns the prime implicants of its dual φ^D. They are dual: mincut() is dual().minpath(), where dual() is the dual structure function φ^D(x) = ~φ(~x).

Which is "path" and which is "cut" depends on how you modeled φ. The method names refer to φ's own implicants; translate them to reliability "path/cut" through your framing:

• Success function (φ = 1 ⟺ the system functions, variable = 1 ⟺ that component functions): minpath() gives the minimal path sets, mincut() the minimal cut sets — matching the method names.
• Fault tree / failure function (φ = 1 ⟺ the top event / system failure, variable = 1 ⟺ that component fails): the two readings swap — minpath() gives the system's minimal cut sets (smallest failure combinations causing the top event), and mincut() gives the minimal path sets.

The fault-tree examples in this README are failure functions, so their minimal cut sets are minpath() — not mincut().

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Make a system (k-of-n gate)
top = bss.kofn(2, [A, B, C])
node = bss.getbdd(top)

# Enumerate the satisfying paths (as a list of sets)
print('All paths which evaluate to one')
for x in node.extract():
    print(x)

# Minimal path vectors of the structure function
min_path = node.minpath().extract()
print('The number of minimal path vectors:', len(min_path))
for x in min_path:
    print(x)

# Minimal cut vectors (= minimal path vectors of the dual)
min_cut = node.mincut().extract()
print('The number of minimal cut vectors:', len(min_cut))
for x in min_cut:
    print(x)

minpath/mincut require a monotone (coherent) structure function (fault trees built from &/|/kofn always are). On a non-monotone function (e.g. one using ^ or ~) they return None:

node = bss.getbdd(A ^ B)     # xor: not monotone
print(node.minpath())        # None
print(node.mincut())         # None

Set algebra on path/cut families

minpath() and mincut() return a ZddNode — a genuine ZDD set family — which supports the set algebra as methods and operators: | union, & intersection, - set difference, * product, / quotient, plus count(), extract(), dot().

bss = ms.BSS()
A, B, C = bss.defvar('A'), bss.defvar('B'), bss.defvar('C')

p = bss.getbdd(A & B | C).minpath()   # { {C}, {A,B} }
q = bss.getbdd(A | C).minpath()       # { {A}, {C} }

print((p | q).count())                # union
print(list((p & q).extract()))        # intersection -> [['C']]
print(list((p - q).extract()))        # difference   -> [['A', 'B']]

Set operations require both families to come from the same BSS context (they share one internal ZDD forest); combining families from different contexts raises ValueError. To build set families from scratch, use the standalone ms.ZDD() manager — see Standalone (low-level) managers.

Draw a BDD

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

top = A & B | C
bdd = bss.getbdd(top)
source = bdd.dot()   # a string in the DOT language
print(source)

# Example: display the BDD in a Jupyter notebook
from graphviz import Source
Source(source)

An example of a large fault tree

top here is a fault tree (a failure function: top = 1 is the top event, each c[i] = 1 is a component failure), so minpath() returns the system's minimal cut sets — see the framing note under Obtain the minimal path / cut vectors.

## Computational time may be long (about 1 minute)

import relibmss as ms

bss = ms.BSS()
c = [bss.defvar("c" + str(i)) for i in range(61)]

g62 = c[0] & c[1]
g63 = c[0] & c[2]
g64 = c[0] & c[3]
g65 = c[0] & c[4]
g66 = c[0] & c[5]
g67 = c[0] & c[6]
g68 = c[0] & c[7]
g69 = c[0] & c[8]
g70 = g62 | c[9]
g71 = g63 | c[10]
g72 = g64 | c[11]
g73 = g65 | c[12]
g74 = g62 | c[13]
g75 = g63 | c[14]
g76 = g64 | c[15]
g77 = g65 | c[16]
g78 = g62 | c[17]
g79 = g63 | c[18]
g80 = g64 | c[19]
g81 = g65 | c[20]
g82 = g62 | c[21]
g83 = g63 | c[22]
g84 = g64 | c[23]
g85 = g65 | c[24]
g86 = g62 | c[25]
g87 = g63 | c[26]
g88 = g64 | c[27]
g89 = g65 | c[28]
g90 = g66 | c[29]
g91 = g68 | c[30]
g92 = g67 | c[31]
g93 = g69 | c[32]
g94 = g66 | c[33]
g95 = g68 | c[34]
g96 = g67 | c[35]
g97 = g69 | c[36]
g98 = g66 | c[37]
g99 = g68 | c[38]
g100 = g67 | c[39]
g101 = g69 | c[40]
g102 = g66 | c[41]
g103 = g68 | c[42]
g104 = g67 | c[43]
g105 = g69 | c[44]
g106 = bss.kofn(3, [g70, g71, g72, g73])
g107 = bss.kofn(3, [g74, g75, g76, g77])
g108 = bss.kofn(3, [g78, g79, g80, g81])
g109 = bss.kofn(3, [g82, g83, g84, g85])
g110 = bss.kofn(3, [g86, g87, g88, g89])
g111 = bss.kofn(3, [g94, g95, g96, g97])
g112 = bss.kofn(3, [g98, g99, g100, g101])
g113 = g90 & g92
g114 = g91 & g93
g115 = g102 & g104
g116 = g103 & g105
g117 = g113 | c[45]
g118 = g114 | c[46]
g119 = g107 | g108 | c[51]
g120 = g109 | g110
g121 = g66 | g117 | c[47]
g122 = g68 | g118 | c[48]
g123 = g67 | g117 | c[49]
g124 = g69 | g118 | c[50]
g125 = bss.kofn(2, [g121, g123, g122, g124])
g126 = g111 | g112 | g125 | c[52]
g127 = g115 & g120
g128 = g116 & g120
g129 = g62 | g127 | c[53]
g130 = g63 | g128 | c[54]
g131 = g64 | g127 | c[55]
g132 = g65 | g128 | c[56]
g133 = g62 | g129 | c[57]
g134 = g63 | g130 | c[58]
g135 = g64 | g131 | c[59]
g136 = g65 | g132 | c[60]
g137 = bss.kofn(3, [g133, g134, g135, g136])
g138 = g106 | g119 | g137
g139 = g62 | g66 | g117 | g129 | c[47]
g140 = g63 | g68 | g118 | g130 | c[48]
g141 = g64 | g67 | g117 | g131 | c[49]
g142 = g65 | g69 | g118 | g132 | c[50]
g143 = g139 & g140 & g141 & g142
g144 = g111 | g112 | g143 | c[52]
top = g126 & g138 & g144

bdd = bss.getbdd(top)
print(bdd.size())      # number of nodes in the BDD

s = bdd.minpath()      # this is a fault tree → minpath() = the system's minimal CUT sets
min_cut = s.extract()
print('The number of minimal cut sets:', len(min_cut))

print('Example: 100 minimal cut sets')
from itertools import islice
for x in islice(min_cut, 0, 100):
    print(x)

Importance analysis

Compute the Birnbaum importance for each event as the first-order derivative of the top-event probability with respect to the probability of the event (assuming independent occurrences).

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

top = A & B | C
node = bss.getbdd(top)

prob = {'A': 0.1, 'B': 0.2, 'C': 0.3}
print(node.prob(prob))
print(node.bmeas(prob))

# top = 1-(1-pa*pb)*(1-pc) = pa*pb+pc-pa*pb*pc
# d top / d pa = pb - pb*pc = 0.2 - 0.2*0.3 = 0.14
# d top / d pb = pa - pa*pc = 0.1 - 0.1*0.3 = 0.07
# d top / d pc = 1 - pa*pb = 1 - 0.1*0.2 = 0.98

# Interval versions
interval_prob = {'A': (0.1, 0.2), 'B': (0.2, 0.3), 'C': (0.3, 0.4)}
print(node.prob_interval(interval_prob))
print(node.bmeas_interval(interval_prob))

# Structure importance measure (all probabilities = 0.5)
print(node.bmeas({'A': 0.5, 'B': 0.5, 'C': 0.5}))

Low-level managers (advanced)

ms.BDD(), ms.MDD(), and ms.ZDD() build directly on nodes, skipping the BSS/MSS expression layer. See Standalone (low-level) managers.

TODO for fault tree analysis

• FTA with MCS
• Importance analysis
• Sensitivity analysis
• Uncertainty analysis; etc.

Multi-state system

Definition of gates

MSS does not have default gates. Users define gates themselves. The operations available in a gate definition are:

• Arithmetic operations: +, -, *, /
• Comparison operations: ==, !=, >, <, >=, <=
• Logical operations:
  • mss.And: AND gate
  • mss.Or: OR gate
  • mss.Not: NOT gate
  • mss.switch: switch-case structure
  • mss.case: case structure
• Value operations:
  • mss.Min: minimum of the given expressions (series-like structure)
  • mss.Max: maximum of the given expressions (parallel-like structure)

Min/Max take a list and are handy when a gate is simply the weakest or strongest of its inputs:

import relibmss as ms

mss = ms.MSS()
X = mss.defvar('X', 3)
Y = mss.defvar('Y', 3)
Z = mss.defvar('Z', 3)

# The system state is the worst (Min) / best (Max) of its components
weakest = mss.Min([X, Y, Z])
strongest = mss.Max([X, Y, Z])

prob = {'X': [0.2, 0.3, 0.5], 'Y': [0.2, 0.3, 0.5], 'Z': [0.2, 0.3, 0.5]}

# P(min == 0) = 1 - 0.8^3 = 0.488
print(mss.getmdd(weakest).prob(prob, [0]))
# P(max == 2) = 1 - 0.5^3 = 0.875
print(mss.getmdd(strongest).prob(prob, [2]))

A larger example using switch/case:

import relibmss as ms

# Define gates
def gate1(mss, x, y):
    return mss.switch([
        mss.case(cond=mss.And([x == 0, y == 0]), then=0),
        mss.case(cond=mss.Or([x == 0, y == 0]), then=1),
        mss.case(cond=mss.Or([x == 2, y == 2]), then=3),
        mss.case(then=2)  # default
    ])

def gate2(mss, x, y):
    return mss.switch([
        mss.case(cond=x == 0, then=0),
        mss.case(then=y)
    ])

mss = ms.MSS()
A = mss.defvar('A', 2)   # 2 states
B = mss.defvar('B', 3)   # 3 states
C = mss.defvar('C', 3)   # 3 states

# Define a multi-state system
sx = gate1(mss, B, C)
ss = gate2(mss, A, sx)

prob = {'A': [0.1, 0.9], 'B': [0.2, 0.3, 0.5], 'C': [0.3, 0.4, 0.3]}

# P(system state in {0, 1, 2})
print(mss.getmdd(ss).prob(prob, [0, 1, 2]))

Draw an MDD

import relibmss as ms

def gate1(mss, x, y):
    return mss.switch([
        mss.case(cond=mss.And([x == 0, y == 0]), then=0),
        mss.case(cond=mss.Or([x == 0, y == 0]), then=1),
        mss.case(cond=mss.Or([x == 2, y == 2]), then=3),
        mss.case(then=2)  # default
    ])

def gate2(mss, x, y):
    return mss.switch([
        mss.case(cond=x == 0, then=0),
        mss.case(then=y)
    ])

mss = ms.MSS()
A = mss.defvar('A', 2)
B = mss.defvar('B', 3)
C = mss.defvar('C', 3)

# Fix the variable order before making the MDD -- see "Variable order" above.
mss.set_varorder(["C", "B", "A"])

sx = gate1(mss, B, C)
ss = gate2(mss, A, sx)

mdd = mss.getmdd(ss)
source = mdd.dot()
print(source)

from graphviz import Source
Source(source)

Obtain the minimal vector sets

import relibmss as ms

def gate1(mss, x, y):
    return mss.switch([
        mss.case(cond=mss.And([x == 0, y == 0]), then=0),
        mss.case(cond=mss.Or([x == 0, y == 0]), then=1),
        mss.case(cond=mss.Or([x == 2, y == 2]), then=3),
        mss.case(then=2)  # default
    ])

def gate2(mss, x, y):
    return mss.switch([
        mss.case(cond=x == 0, then=0),
        mss.case(then=y)
    ])

mss = ms.MSS()
A = mss.defvar('A', 2)
B = mss.defvar('B', 3)
C = mss.defvar('C', 3)

sx = gate1(mss, B, C)
ss = gate2(mss, A, sx)

s = mss.getmdd(ss).minpath()   # a ZmddNode: family of minimal path vectors
# extract(values) enumerates the vectors filed under a performance label in `values`
# (dense: every variable is listed, unrecorded components at 0)
for path in s.extract([1, 2, 3]):
    print(path)

extract(values) selects strata, not levels. Each vector is filed under the label equal to its own performance φ(x). The classical "minimal path vectors to level v" (minimal{x : φ(x) >= v}) and "minimal cut vectors to level v" (maximal{x : φ(x) <= v}) are extract_level(v). The two agree at the lowest and highest labels but differ in between — in the example above A = 0 alone drives φ to 0, so {A:0, B:2, C:2} sits in stratum 0 while still being a genuine cut vector for levels 1 and 2:

cut = mss.getmdd(ss).mincut()
cut.extract([1])        # stratum 1 -> {A:1,B:0,C:2}, {A:1,B:2,C:0}
cut.extract_level(1)    # level 1   -> the two above, plus {A:0,B:2,C:2}

labels() lists the labels a family stratifies over and is_cut() says which kind it is. Every family also contains the baseline member — the all-0 vector for a path family, the all-max vector for a cut family — which is a correct but trivial vector, usually skipped.

mincut() is the dual — the minimal cut vectors (the smallest deviations below max that hold the system down to a level). A cut vector is reported dense, with the components it does not push down sitting at their max state, and extract(values) selects the resulting performance level in the structure function's own scale:

mss = ms.MSS()
X, Y, Z = mss.defvar('X', 3), mss.defvar('Y', 3), mss.defvar('Z', 3)
phi = mss.getmdd(mss.Max([mss.Min([X, Y]), Z]))   # φ = max(min(X, Y), Z)

# to hold φ down to level 0 you need Z=0 AND (X=0 or Y=0):
print(list(phi.mincut().extract([0])))
# -> [{'X': 0, 'Y': 2, 'Z': 0}, {'X': 2, 'Y': 0, 'Z': 0}]   (unrecorded components at max)

It is computed directly (the engine never builds the expensive multi-state dual MDD) and, like minpath, returns a ZmddNode (None if the function is not coherent).

minpath requires a coherent (monotone) structure function; it returns None when the function is not coherent. The result is a ZmddNode — the multi-state analogue of the BSS ZddNode: a family of minimal path vectors (each a dense {var: state} dict) stratified by the performance label they reach. It supports label-wise set operations — & intersection, - set difference — plus count(values) / extract(values) / extract_level(level):

mss = ms.MSS()
X = mss.defvar('X', 3)
Y = mss.defvar('Y', 3)
Z = mss.defvar('Z', 3)

# φ = max(min(X, Y), Z): minimal path vectors {Z=1}, {Z=2}, {X=1,Y=1}, {X=2,Y=2}
a = mss.getmdd(mss.Max([mss.Min([X, Y]), Z])).minpath()
# min(X, Y): minimal path vectors {X=1,Y=1}, {X=2,Y=2}
b = mss.getmdd(mss.Min([X, Y])).minpath()

print(list((a & b).extract([1, 2])))
# intersection -> [{'X': 1, 'Y': 1, 'Z': 0}, {'X': 2, 'Y': 2, 'Z': 0}]
print(list((a - b).extract([1, 2])))
# difference   -> [{'X': 0, 'Y': 0, 'Z': 1}, {'X': 0, 'Y': 0, 'Z': 2}]
print((a - b).count([1, 2]))           # size of the difference -> 2

Set operations require both families to come from the same MSS context (they share one internal ZMDD forest); combining families from different contexts raises ValueError.

Importance analysis

bmeas(probability, values) returns the multi-state Birnbaum importance of every variable for the success set values. For a variable with M states it returns M-1 numbers — one per state boundary — where D_j = P(φ∈values | var=j) − P(φ∈values | var=j−1) is the importance of raising that component across the j−1 → j boundary (the multi-state generalization of the BSS Birnbaum measure, which is the binary case). Computed in one backward-differentiation pass.

import relibmss as ms

mss = ms.MSS()
X = mss.defvar('X', 3)
Y = mss.defvar('Y', 3)
Z = mss.defvar('Z', 3)

node = mss.getmdd(mss.Max([mss.Min([X, Y]), Z]))   # φ = max(min(X, Y), Z)
prob = {'X': [0.2, 0.3, 0.5], 'Y': [0.5, 0.1, 0.4], 'Z': [0.25, 0.25, 0.5]}

# success = performance level >= 1
print(node.bmeas(prob, [1, 2]))
# X -> [0.125, 0.0],  Y -> [0.2, 0.0],  Z -> [0.6, 0.0]   (each is [D_1, D_2])
# e.g. D_{Y,1} = P(φ>=1 | Y=1) - P(φ>=1 | Y=0) = 0.95 - 0.75 = 0.20
#   (the second entry is 0.0 here because raising a component from state 1 to 2
#    never changes whether φ>=1)

# Interval version: each per-state probability is a (lo, hi) bound
interval_prob = {'X': [(0.2, 0.2), (0.3, 0.3), (0.5, 0.5)],
                 'Y': [(0.5, 0.5), (0.1, 0.1), (0.4, 0.4)],
                 'Z': [(0.25, 0.25), (0.25, 0.25), (0.5, 0.5)]}
print(node.bmeas_interval(interval_prob, [1, 2]))

bmeas_interval returns a guaranteed but conservative enclosure: for every point probability inside the given (lo, hi) boxes the true importance lies within the returned interval (a degenerate box lo == hi reproduces bmeas exactly). It is not the tightest enclosure — interval arithmetic's dependency problem, together with the difference P(φ|var=j) − P(φ|var=j−1) being evaluated as a worst-case interval subtraction, widens the bounds (the interval can even straddle 0 when the true value has a definite sign). As with prob_interval, the constraint sum_j p[var][j] == 1 is not enforced — the per-state bounds are treated independently.

Dependent events (common-cause failures)

prob multiplies per-variable probabilities along the diagram, so it assumes the events are s-independent. Dependence is expressed by modelling its cause explicitly: introduce a variable for the common cause and make the affected components functions of it. The components are then conditionally independent given that variable, which is exactly what the diagram computes — it evaluates Σ_z P(Z = z) · P(system | Z = z) on its own, with no extra API.

import relibmss as ms

# A and B share a common cause Z: when Z fails, both fail.
#   A_eff = Z & A_own,  B_eff = Z & B_own
bss = ms.BSS()
Z, A_own, B_own = bss.defvar('Z'), bss.defvar('A_own'), bss.defvar('B_own')
parallel = bss.getbdd((Z & A_own) | (Z & B_own))

p = parallel.prob({'Z': 0.9, 'A_own': 0.8, 'B_own': 0.7})
print(p)                       # 0.8460000000000001, i.e. 0.9 * (1 - 0.2 * 0.3)

Feeding the two marginal probabilities to an independent model instead (P(A_eff) = 0.9 * 0.8 = 0.72, P(B_eff) = 0.9 * 0.7 = 0.63) gives 1 - 0.28 * 0.37 = 0.8964 — optimistic by 5 points, because it ignores that A and B fail together.

The multi-state side works the same way. Here Z = 0 drags both components down to state 0:

import relibmss as ms

mss = ms.MSS()
Z = mss.defvar('Z', 2)
A_own, B_own = mss.defvar('A_own', 3), mss.defvar('B_own', 3)
A_eff = mss.switch([mss.case(cond=Z == 0, then=0), mss.case(then=A_own)])
B_eff = mss.switch([mss.case(cond=Z == 0, then=0), mss.case(then=B_own)])
node = mss.getmdd(mss.Max([A_eff, B_eff]))       # parallel: performance = max

prob = {'Z': [0.1, 0.9], 'A_own': [0.2, 0.3, 0.5], 'B_own': [0.3, 0.2, 0.5]}
print(node.prob(prob, [2]))    # 0.675   (independent marginals would give 0.6975)
print(node.prob(prob, [1, 2])) # 0.8460000000000001

bmeas inherits the same treatment — with the common cause modelled as a variable it also reports the importance of that common cause. Note, though, that Birnbaum importance is a derivative ∂R/∂p_i, so it presumes each p_i can be moved on its own; between genuinely dependent components that premise does not hold, and the number should be read as the importance of the modelled variables, not of the correlated events.

Minimal path / cut vectors do not help here: they are a structural property of the structure function and are the same whether or not the components are dependent. What dependence changes is how the probability of the family is computed, so minpath() / mincut() are not a route around the independence assumption.

TODO

• Add more examples
• Add more functions for fault tree analysis
• Add more functions for multi-state system analysis

License

MIT License