# Exam P Formula Sheet

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SOA/CAS P/1
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Actuarial Science Exam 1/P Ville A. Satop¨a¨aDecember 5, 2009 Contents 1 Review of Algebra and Calculus 22 Basic Probability Concepts 33 Conditional Probability and Independence 44 Combinatorial Principles, Permutations and Combinations 55 Random Variables and Probability Distributions 56 Expectation and Other Distribution Parameters 67 Frequently Used Discrete Distributions 78 Frequently Used Continuous Distributions 89 Joint, Marginal, and Conditional Distributions 1010 Transformations of Random Variables 1111 Risk Management Concepts 13 1  1 Review of Algebra and Calculus 1. The complement of the set  B : This can be denoted  B  , ¯ B , or  ∼ B .2. For any two sets we have:  A  = ( A ∩ B ) ∪ ( A ∩ B  )3. The inverse of a function exists only if the function is one-to-one.4. The roots of a quadratic equation  ax 2 + bx + c  can be deduced from − b ±√  b 2 − 4 ac 2 a Recall that the equation has distinct roots if   b 2 − 4 ac >  0, distinct complex roots if   b 2 − 4 ac <  0, or equalreal roots if   b 2 − 4 ac  = 0.5. Exponential functions are of the form  f  ( x ) =  b x , where  b >  0,  b  = 1. The inverse of this function is denotedlog b ( y ). Recall that b log b ( y ) =  y  for  y >  0 b x =  e x log( b ) 6. A function  f   is continuous at the point  x  =  c  if lim s → c f  ( x ) =  f  ( c ).7. The algebraic deﬁnition of   f   ( x 0 ) is d 1 f dx 1  x = x 0 =  f  (1) ( x 0 ) =  f   ( x 0 ) = lim h → 0 f  ( x 0  + h ) − f  ( x 0 ) h 8. Diﬀerentation: Product and quotient rule g ( x ) × h ( x )  →  g  ( x ) × h ( x ) + g ( x ) × h  ( x ) g ( x ) h ( x )  →  h ( x ) g  ( x ) − g ( x ) h  ( x )[ h ( x )] 2 and some other rules a x →  a x log( a )log b ( x )  →  1 x log( b )sin( x )  →  cos( x )cos( x )  → − sin( x )9. L’Hopital’s rule: if lim x → c f  ( x ) = lim x → c f  ( x ) = 0 or  ±∞  and lim x → c f   ( x ) /g  ( x ) exists, thenlim x → c f  ( x ) g ( x ) = lim x → c f   ( x ) g  ( x )10. Integration: Some rules1 x  →  log( x ) + ca x →  a x log( a ) + cxe ax →  xe ax a  −  e ax a 2  + c 2  11. Recall that    ∞−∞ f  ( x ) dx  = lim a →∞    a − a f  ( x ) dx This can be useful if the integral is not deﬁned at some point  a , or if   f   is discontinuous at  x  =  a ; then wecan use    ba f  ( x ) dx  = lim c → a    bc f  ( x ) dx Similarly, if   f  ( x ) has discontinuity at the point  x  =  c  in the interior of [ a,b ], then    ba f  ( x ) dx  =    ca f  ( x ) dx +    bc f  ( x ) dx Let’s do one example to clarify this a little bit:    10 x − 1 / 2 dx  = lim c → 0    1 c x − 1 / 2 dx  = lim c → 0  (1 / 2) x 1 / 2  1 c   = lim c → 0 [2 − 2 √  c ] = 2More examples on page. 1812. Some other useful integration rules are:(i) for integer  n ≥ 0 and real number  c >  0, we have   ∞ 0  x n e − cx dx  =  n ! c n +1 13. Geometric progression: The sum of the ﬁrst  n  terms is a + ar  + ar 2 + ... + ar n − 1 =  a [1 + r  + r 2 + ... + r n − 1 ] =  a ×  r n − 1 r − 1 =  a ×  1 − r n 1 − r and ∞  k =0 ar k =  a 1 − r 14. Arithmetic progression: The sum of the ﬁrst  n  terms is a + ( a + d ) + ( a + 2 d ) + ... + ( a + nd ) =  na + d ×  n ( n − 1)2 2 Basic Probability Concepts 1. Outcomes are exhaustive if they combine to be the entire probability space, or equivalently, if at least on of the outcomes must occur whenever the experiment is performed. In other words, if   A 1 ∪ A 2 ∪ ... ∪ A n  = Ω,then  A 1 ,A 2 ,...,A n  are referred to as exhaustive events.2. Example 1-1 on page 37 summarizes many key deﬁnitions very well.3. Some useful operations on events:(i) Let  A,B 1 ,B 2 ,...,B n  be any events. Then A ∩ ( B 1 ∪ B 2 ∪ ... ∪ B n ) = ( A ∩ B 1 ) ∪ ( A ∩ B 2 ) ∪ ... ∪ ( A ∩ B n )and A ∪ ( B 1 ∩ B 2 ∩ ... ∩ B n ) = ( A ∪ B 1 ) ∩ ( A ∪ B 2 ) ∩ ... ∩ ( A ∪ B n )3  (ii) If   B 1 ∪ B 2  = Ω, then  A  = ( A ∩ B 1 ) ∪ ( A ∩ B 2 ). This of course applies to a partition of any size. To putin this in the context of probabilities we have  P  ( A ) =  P  ( A ∩ B 1 ) + P  ( A ∩ B 2 )3. An event  A  consists of a subset of sample points in the probability space. In the case of a discrete probabilityspace, the probability of   A  is  P  ( A ) =   a 1 ∈ A P  ( a i ), the sum of   P  ( a 1 ) over all sample points in event  A .4. An important inequality: P  ( n  i =1 A i ) ≤ n  i =1 P  ( A i )Notice that the equality holds only if they are mutually exclusive. Any overlap reduces the total probability. 3 Conditional Probability and Independence 1. Recall the multiplication rule:  P  ( B ∩ A ) =  P  ( B | A ) P  ( A )2. When we condition on event  A , we are assuming that event  A  has occurred so that  A  becomes the newprobability space, and all conditional events must take place within event  A . Dividing by  P  ( A ) scales allprobabilities so that  A  is the entire probability space, and  P  ( A | A ) = 1.3. A useful fact:  P  ( B ) =  P  ( B | A ) P  ( A ) + P  ( B | A  ) P  ( A  )4. Bayes’ rule:(i) The basic form is  P  ( A | B ) =  P  ( A ∩ B ) P  ( B )  =  P  ( B | A ) P  ( A ) P  ( B )  . This can be expanded even further as follows. P  ( A | B ) =  P  ( A ∩ B ) P  ( B ) =  P  ( A ∩ B ) P  ( B ∩ A ) + P  ( B ∩ A  ) =  P  ( B | A ) P  ( A ) P  ( B | A ) P  ( A ) + P  ( B | A  ) P  ( A  )(ii) The extended form. If   A 1 ,A 2 ,...,A n  form a partition of the entire probability space Ω, then P  ( A i | B ) =  P  ( B ∩ A i ) P  ( B ) =  P  ( B ∩ A i )  ni =1 P  ( B ∩ A i ) =  P  ( B | A i ) P  ( A i )  ni =1 P  ( B | A i ) P  ( A i ) for each  i  = 1 , 2 ,...,n 5. If events  A 1 ,A 2 ,...,A n  satisfy the relationship P  ( A 1 ∩ A 2 ∩ ... ∩ A n ) = n  i =1 P  ( A i )then the events are said to be mutually exclusive.6. Some useful facts:(i) If   P  ( A 1 ∩ A 2 ∩ ... ∩ A n − 1 )  >  0, then P  ( A 1 ∩ A 2 ∩ ... ∩ A n ) =  P  ( A 1 ) × P  ( A 2 | A 1 ) × P  ( A 3 | A 1 ∩ A 2 ) × ... × P  ( A n | A 1 ∩ A 2 ∩ ... ∩ A n − 1 )(ii)  P  ( A  | B ) = 1 − P  ( A | B )(iii)  P  ( A ∪ B | C  ) =  P  ( A | C  ) + P  ( B | C  ) − P  ( A ∩ B | C  )4

Jul 22, 2017

#### HW5

Jul 22, 2017
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