best book on probability

Original writing, reproduced please indicate the source of http://www.cnblogs.com/xbinworld/archive/2013/04/25/3041505.html Today I will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I) This section describes the essence of probability theory in the entire book, highlighting an uncertainty understanding

Original writing. For reprint, please indicate that this article is from:Http://blog.csdn.net/xbinworld, Bin Column Pattern Recognition and machine learning (PRML), Chapter 1.2, probability theory (I) This section describes the essence of probability theory in the entire book, highlighting an uncertainty understanding. I think it is slow. I want to take a loo

is a good student. He just applied for the scholarship p1 = P (A1) * P (b1 | A1) = 0.4*0.3 = 0.12; this student is a four-student. He just got the scholarship as a four-student. P2 = P (A2) * P (B2 | A2) = 0.3*0.4 = 0.12; this student is a five-year-old student and has just received a scholarship as a five-year-old student. P3 = P (A3) * P (B3 | A3) = 0.2*0.5 = 0.10; this student is a good six student. He just got the scholarship as a good six student. P4 = P (A4) * P (B4 | A4) = 0.1*0.6 = 0.06

IOS Address Book programming, listening for system address book changes, and ios address book Listen for address book changes The client code must be implemented as follows: /* Remove the registration function */-(void) dealloc {ABAddressBookUnregisterExternalChangeCallback (_ addressBook, ContactsChangeCallback, nil)

The learning content of probability theory and mathematical statistics originates from Chinese university MOOC, and the fourth edition of the book "Probability Theory and Mathematical Statistics", Zhejiang University.Random PhenomenaUnder certain conditions, it is possible to have a variety of results, and can not know the result before it happens.Randomised Tria

likelihood solution. For finite data sets, the posteriori mean of parameter μ is always between the transcendental average and the maximum likelihood estimate of μ.SummarizeAs we can see, the posterior distribution becomes an increasingly steep peak shape as the observational data increases. This is shown by the variance of the beta distributions, when a and b approach infinity, the variance of the beta distribution tends to be nearly 0. At a macro level, when we observe more data, the uncertai

(expect the farmers to get the right to license ...) The number of hands above both can walk the card and high top of the 2 to do "can go Card hand not put", there are aaa,222 licensing the crossroads to do "Choose the landlord as small as possible probability" The above example can also be regarded as a loss of the game after the re-disk, why the top 2, and not the top K, any step of the card can be found, this re-disk is practical meaning that the

Hmm (a) hmm model of hidden Markov modelHmm (second) forward backward algorithm of hidden Markov model to evaluate the probability of observation sequenceHmm (three) Baum-Welch algorithm for hmm parameter (TODO) in hidden Markov modelHmm (four) Viterbi algorithm decoding hidden State sequence (TODO) by Hidden Markov modelIn hmm (a) hmm model of hidden Markov model, we talk about the basic knowledge of HMM and the three basic problems of Hmm, we focus

Drinking games, probability distributions and convolutionI've always had a passion for things that could count probabilities, including a little game when I was drinking.Put a cup on the table, a table of people take turns to roll the dice, shaking two at a time. If the two dice result number and Y are not any of {7,8,9}, this player counted, without drinking, to the next person to shake, but if y=7, the player in the cup to pour alcohol, can be less,

We finish the process of PHP backstage, PHP's main work is responsible for the allocation of prizes and the corresponding probability of winning, the current page click to flip a box will want the background PHP to send Ajax request, then the backstage PHP according to the probability of configuration, through the probability algorithm to give the winning results

In the algorithm introduction book see a more interesting probability algorithm, here add their own understanding to share under: The last time I just saw the friends of the circle said:" two people in the same dormitory, and the same year, the same day, this fate is really drunk ", I was drunk, looked at the algorithm after the discovery, the house has a person, then you can The

a-robberies The aspiring Roy the robber has seen a lot of American movies, and knows the bad guys usually the gets Often because they become too greedy. He has decided to work in the lucrative business of bank robbery only for a short while, before retiring to a comfortable Job at a university. For a few months now, Roy has been assessing the security of various banks and the amount of cash they. He wants to make a calculated risk, and grab as much as possible. His mother, Ola, has decided up

TF-IDF model calculates a weight based on the query string Q composed of the TF and IDF for each document D and the keyword W[1]...w[k], which is used to represent the matching degree of query string Q to document D:The probability perspective of information retrieval problemIntuitively, TF describes the frequency at which the document Morphemes appears, while IDF is the weight associated with the number of documents that the word appears. It is easi

Note : This is a task spanning several years, and the title can also be called "learning statistics from the To Do list." When I was distressed by P-values a few years ago, I didn't know what Python was, and then, after touching Python, I liked the language. Statistics as the basis of data science, want to do this work, this is always a way around the sill.In fact, from the middle school began to study statistics, the earliest write "positive" character recount (equivalent to find the majority),

 Original: http://blog.csdn.net/marvin521/article/details/11489453 04. Application Example of probability graph model A recent article "Deformable Model Fitting by regularized Landmark Mean-shift" in the Human Face detection algorithm in the speed and precision of the compromise reached a relatively good level, This technical report will explain the working principle of the algorithm and the related matting algorithm. Before this article, first of

Reprint http://noalgo.info/414.htmlProbability theory is one of the most important basic subjects in computer science, and the probability problem is also a frequently encountered issue in the process of job search.Here is a summary of some of the classic probability questions as an exercise.1. Randomly select a point in a circle with a radius of 1.Method 1: Randomly select a point on the x-axis [ -1,1],y a

Classification method based on probability theory in Python programming: Naive Bayes and python bayesian Probability Theory and probability theory are almost forgotten. Probability theory-based classification method: Naive Bayes 1. Overview Bayesian classification is a general term for classification algorithms. These

PHP winning probability algorithm, can be used for scraping cards, large turntable, such as lottery algorithm. The usage is very simple, the code has the detailed annotation explanation, at a glance can understand $proCur) { $randNum = Mt_rand (1, $proSum); if ($randNum $result = $key; Break } else { $proSum-= $proCur; } } Unset ($PROARR); return $result; } /* * Awards Array * is a two-dimensional array that records all the awards info

Question: You have to send a package to the post office. There are n windows, each of which has a number of people. The TI distribution at each window is in line with the geometric distribution: Ki * E ^ (-ki * t) The current service has performed CI time in each window. You will go to the first window to complete the current service. Please expect the total time from arriving at the post office to sending the package It is said to be a question in the prob