• Complain

Massimiliano Bonamente - Statistics and Analysis of Scientific Data

Here you can read online Massimiliano Bonamente - Statistics and Analysis of Scientific Data full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 0, publisher: Springer New York, New York, NY, genre: Children. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Massimiliano Bonamente Statistics and Analysis of Scientific Data
  • Book:
    Statistics and Analysis of Scientific Data
  • Author:
  • Publisher:
    Springer New York, New York, NY
  • Genre:
  • Year:
    0
  • Rating:
    4 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 80
    • 1
    • 2
    • 3
    • 4
    • 5

Statistics and Analysis of Scientific Data: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Statistics and Analysis of Scientific Data" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

Massimiliano Bonamente: author's other books


Who wrote Statistics and Analysis of Scientific Data? Find out the surname, the name of the author of the book and a list of all author's works by series.

Statistics and Analysis of Scientific Data — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Statistics and Analysis of Scientific Data" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Springer Science+Busines Media New York 2017
Massimiliano Bonamente Statistics and Analysis of Scientific Data Graduate Texts in Physics 10.1007/978-1-4939-6572-4_1
1. Theory of Probability
Massimiliano Bonamente 1
(1)
University of Alabama, Huntsville, Alabama, USA
Abstract
The theory of probability is the mathematical framework for the study of the probability of occurrence of events. The first step is to establish a method to assign the probability of an event, for example, the probability that a coin lands heads up after a toss. The frequentist or empiricalapproach and the subjective or Bayesian approach are two methods that can be used to calculate probabilities. The fact that there is more than one method available for this purpose should not be viewed as a limitation of the theory, but rather as the fact that for certain parts of the theory of probability, and even more so for statistics, there is an element of subjectivity that enters the analysis and the interpretation of the results. It is therefore the task of the statistician to keep track of any assumptions made in the analysis, and to account for them in the interpretation of the results. Once a method for assigning probabilities is established, the Kolmogorov axioms are introduced as the rules required to manipulate probabilities. Fundamental results known as Bayes theorem and the theorem of total probability are used to define and interpret the concepts of statistical independence and of conditional probability, which play a central role in much of the material presented in this book.
1.1 Experiments, Events, and the Sample Space
Every experiment has a number of possible outcomes. For example, the experiment consisting of the roll of a die can have six possible outcomes, according to the number that shows after the die lands. The sample space is defined as the set of all possible outcomes of the experiment, in this case ={ 1,2,3,4,5,6}. An event A is a subset of , A , and it represents a number of possible outcomes for the experiment. For example, the event even number is represented by A ={ 2,4,6}, and the event odd number as B ={ 1,3,5}. For each experiment, two events always exist: the sample space itself, , comprising all possible outcomes, and A =, called the impossible event , or the event that contains no possible outcome.
Events are conveniently studied using set theory, and the following definitions are very common in theory of probability:
  • The complementary Picture 1 of an event A is the set of all possible outcomes except those in A . For example, the complementary of the event odd number is the event even number.
  • Given two events A and B , the union C = A B is the event comprising all outcomes of A and those of B . In the roll of a die, the union of odd and even numbers is the sample space itself, consisting of all possible outcomes.
  • The intersection of two events C = A B is the event comprising all outcomes of A that are also outcomes of B . When A B =, the events are said to be mutually exclusive . The union and intersection can be naturally extended to more than two events.
  • A number of events A i are said to be a partition of the sample space if they are mutually exclusive, and if their union is the sample space itself, A i =.
  • When all outcomes in A are comprised in B , we will say that A B or B A .
1.2 Probability of Events
The probability P of an event describes the odds of occurrence of an event in a single trial of the experiment. The probability is a number between 0 and 1, where P =0 corresponds to an impossible event, and P =1 to a certain event. Therefore the operation of probability can be thought of as a function that transforms each possible event into a real number between 0 and 1.
1.2.1 The Kolmogorov Axioms
The first step to determine the probability of the events associated with a given experiment is to establish a number of basic rules that capture the meaning of probability. The probability of an event is required to satisfy the three axioms defined by Kolmogorov [] :
  1. The probability of an event A is a non-negative number, P ( A )0;
  2. The probability of all possible outcomes, or sample space, is normalized to the value of unity, P ()=1;
  3. If A and B are mutually exclusive events, then
    11 Figure illustrates this property using set diagrams For events that are - photo 2
    (1.1)
    Figure illustrates this property using set diagrams. For events that are not mutually exclusive, this property does not apply. The probability of the union is represented by the area of A B , and the outcomes that overlap both events are not double-counted.
    Fig 11 The probability of the event P A B is the sum of the two - photo 3
    Fig. 1.1
    The probability of the event P ( A B ) is the sum of the two individual probabilities, only if the two events are mutually exclusive. This property enables the interpretation of probability as the area of a given event within the sample space
These axioms should be regarded as the basic ground rules of probability, but they provide no unique specification on how event probabilities should be assigned. Two major avenues are available for the assignment of probabilities. One is based on the repetition of the experiments a large number of times under the same conditions, and goes under the name of the frequentist or classical method. The other is based on a more theoretical knowledge of the experiment, but without the experimental requirement, and is referred to as the Bayesian approach.
1.2.2 Frequentist or Classical Method
Consider performing an experiment for a number N 1 of times, under the same experimental conditions, and measuring the occurrence of the event A as the number N ( A ). The probability of event A is given by
Statistics and Analysis of Scientific Data - image 4
(1.2)
that is, the probability is the relative frequency of occurrence of a given event from many repetitions of the same experiment. The obvious limitation of this definition is the need to perform the experiment an infinite number of times, which is not only time consuming, but also requires the experiment to be repeatable in the first place, which may or may not be possible.
The limitation of this method is evident by considering a coin toss: no matter the number of tosses, the occurrence of heads up will never be exactly 50%, which is what one would expect based on a knowledge of the experiment at hand.
1.2.3 Bayesian or Empirical Method
Another method to assign probabilities is to use the knowledge of the experiment and the event, and the probability one assigns represents the degree of belief that the event will occur in a given try of the experiment. This method implies an element of subjectivity, which will become more evident in Bayes theorem (see Sect. ].
Example 1.1 (Coin Toss Experiment)
In the coin toss experiment, the determination of the empirical probability for events heads up or tails up relies on the knowledge that the coin is unbiased, and that therefore it must be true that P ( tails )= P ( heads ). This empirical statement signifies the use of the Bayesian method to determine probabilities. With this information, we can then simply use the Kolmogorov axioms to state that P ( tails ) + P ( heads )=1, and therefore obtain the intuitive result that P ( tails )= P ( heads )=12. Picture 5
Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Statistics and Analysis of Scientific Data»

Look at similar books to Statistics and Analysis of Scientific Data. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Statistics and Analysis of Scientific Data»

Discussion, reviews of the book Statistics and Analysis of Scientific Data and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.