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What is the smallest value of k for which $a^k \equiv 1 \mod m$ when choosing a suitable parameter for generating random numbers?
The probability for each face of a fair six-sided die is equal.
True
What is the empirical mean for the results of rolling a six-sided die 10,000 times, based on the given data?
3.6192
In the context of generating random numbers, the output after performing the operation is denoted as $x_k = a imes x_{k-1} \mod m$, and $x_k$ represents the ______.
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Match each term with its correct description:
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What kind of distribution is expected when rolling a fair die multiple times?
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When generating pseudo-random numbers, the period will always be the same no matter the choice of parameters.
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What command in Python is used to generate a random integer between 1 and 6?
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What is the probability of rolling an odd number when rolling a fair six-sided die?
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In a discrete probability space, the sum of the probabilities of all possible outcomes equals 1.
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If the sample space is Ω = {1, 2, 3, 4}, what is the probability function p(ω) if it is uniformly distributed?
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The event of rolling a number greater than 4 is represented by the set ______.
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Match the following events with their descriptions:
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What is the correct formula for the probability of an event A in a discrete probability space?
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If A and B are two events such that A∩B = ∅, they are considered independent events.
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What does it mean for p(ω) to be defined as p(ω) = 1/#Ω in a uniform distribution?
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What is the formula for calculating the unbiased empirical variance?
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In a discrete probability space, the sum of probabilities of all outcomes must equal 1.
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What are the three axioms of probability defined by Kolmogorov?
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A probability function maps events to [0, 1]. This can be written as __________.
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Match the following terms with their definitions:
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If the sample space is $ ext{Ω} = ext{Set of natural numbers}$, what does $ ext{P(Ω)}$ represent?
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An event can have a probability greater than 1.
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What is the median of the data set {2, 4, 6, 8}?
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What is the condition for using the Poisson approximation according to the theorem?
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For any A ⊆ Ω, A ∩ B ⊆ B holds true.
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What does the conditional distribution P(A | B) represent?
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The _____ of total probability helps in calculating probabilities when there are multiple disjoint events.
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Match the following definitions with their corresponding terms:
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According to Proposition 3.2, what is true about P( · | B)?
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The statement P(Ω | B) = P(B) is equal to 1.
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What is the requirement for P(B) in the definition of conditional distribution?
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What is the probability that an individual is sick given a positive test result based on the calculations provided?
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Events A and B are independent if and only if P(A ∩ B) is equal to P(A) + P(B).
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If P(A|B) is 0.99 and P(B) is 0.001, what is the value of P(B|A) in the provided equation?
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The probability of multiple events occurring can be expressed as P(A1 ∩ A2) = P(A1) · P(A2 | A1). Fill in the blank: P(A1 ∩ A2 ∩ A3) = P(A1) · P(A2 | A1) · P(A3 | A1 ∩ ____.
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Match the following terms with their definitions:
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In the discrete probability space (Ω, P), which of the following is a property of independent events?
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The total probability theorem allows for the calculation of P(B|A) using P(A|B) and P(B).
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The formula for conditional probability is P(A|B) = P(A ∩ B) / P(___).
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Study Notes
Algorithm 1 for Generating Random Numbers
-
The algorithm generates a sequence of pseudorandom numbers using modulo arithmetic.
-
Input:
-
x0
(initial value, between 0 and 1) -
m
(large prime number) -
a
(natural number)
-
-
Output:
- Sequence of numbers
x1, x2, ... , xn
- Sequence of numbers
-
Operation:
- The algorithm iterates through
k
from 1 ton
, wheren
is desired number of random numbers, and for eachk
, it calculates:xk = a*xk-1 mod m;
- The algorithm iterates through
-
Choosing
a
:- To ensure good randomness,
a
must be chosen such that the smallestk
for whichak ≡ 1 mod m
isk = m - 1
. - With correct
a
, the algorithm generatesn = m - 1
unique pseudo-random numbers before they start repeating.
- To ensure good randomness,
Rolling a Dice
-
Sample space for a six-sided dice:
- {1, 2, 3, 4, 5, 6}
-
Probability for each face (fair dice):
- P({1}) = P({2}) = ... = P({6}) = 1/6
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Python implementation:
-
dice = [random.randint(1, 6) for i in range(10000)]
- This creates a list of 10000 random numbers between 1 and 6.
-
-
Mean for a random sample:
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Formula:
x = (1/N) * Σ(xi)
whereN
is the sample size andxi
are the individual values of the random sample.
-
Formula:
-
Empirical variance:
-
Formula:
v̂ = (1/N) * Σ(xi - x)^2
, it measures the deviation from the mean -
Unbiased variance:
v = (1/(N-1)) * Σ(xi - x)^2
, it is calculated as N-1 in the denominator.
-
Formula:
-
Empirical standard deviation:
-
Formula:
s = √v
andŝ = √v̂
-
Formula:
-
Median for a random sample:
-
Formula:
median(x) = (y(n+1)/2)
if n is odd,median(x) = (y(n/2))
if n is even.
-
Formula:
Discrete Probability Spaces
- Sample space (Ω): A set of all possible outcomes of an experiment.
- Outcomes (ω): Individual elements of the sample space.
- Events (A): Subsets of the sample space, representing specific combinations of outcomes.
- Power set (P(Ω)): The set of all possible subsets of the sample space, including the empty set and the sample space itself.
Probability
- Probability function (P): A mapping from the power set of the sample space to real numbers between 0 and 1.
-
Kolmogorov's axioms:
-
Axiom 1: For any event
A
,0 ≤ P(A) ≤ 1
. -
Axiom 2:
P(Ω) = 1
. -
Axiom 3: For any countable set of pairwise disjoint events (
Ai ∩ Aj = ∅
) :P(∪Ai) = ΣP(Ai)
.
-
Axiom 1: For any event
- Discrete probability space (Ω, p): A sample space Ω (at most countable) and a probability function p: Ω→[0, 1] where the sum of probabilities for all outcomes equals 1.
Conditional Probability and Independence
-
Conditional probability:
P(A|B) = P(A ∩ B) / P(B)
- Represents the probability of event A happening given that event B has already occurred.
- Requires
P(B) > 0
.
-
Law of total probability:
- For a set of events (B1, B2, ..., Bk) that form a partition of the sample space (meaning they are mutually exclusive and cover the whole sample space), then
P(A) = P(A ∩ B1) + P(A ∩ B2) + ... + P(A ∩ Bk)
- For a set of events (B1, B2, ..., Bk) that form a partition of the sample space (meaning they are mutually exclusive and cover the whole sample space), then
-
Bayes' theorem:
-
P(B | A) = (P(A | B) * P(B)) / P(A)
, used to calculate the probability of an event given that another event has occurred.
-
-
Independence of events:
- Events A and B are independent if:
P(A ∩ B) = P(A) * P(B)
. This means that the occurrence of one event does not affect the probability of the other.
- Events A and B are independent if:
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Description
This quiz covers the algorithm for generating pseudorandom numbers using modulo arithmetic and the probability concepts related to rolling a fair six-sided dice. Participants will explore how initial values and parameters affect randomness, as well as basic probability calculations for dice outcomes.