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algorithms to live by bayes rule

# Now that we have `masked`, we will actually be picking the first !NaN value. …” ― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions From A/B Testing websites to A/B Testing human drugs via clinical trials, software engineers and pharmaceutical companies alike are trying to figure out where the balance lies. Sharing points: 1. If you pass on someone, you cannot come back to them. # Finding the number of times we made the best choice at this point is easy. Just make sure your priors are good: a good reminder in this chapter was that exposure to just news and not much else serves to contaminate them, making us worse predictors of events. Obviously you can not sort your socks but imagine there were numbers between 0 to 19 in the bag. # 10 17 3 88 59 13 # 9 49 3 1 5 53, # Figure out the first value > threshold. The perfect is the enemy of the good, so it’s okay to just relax and let it slide once in a while. # 0 NaN NaN NaN 88.0 NaN Imagine the following scenario: you have to hire a secretary from a pool of fixed applicants. The longer the incidents goes on, assume it might finish any given time. If we repeat an experiment that we know can result in a success or failure, n times independently, and get s successes, then what is the probability that the next repetition will succeed? It turned out it was power-law distribution after all, and he lived twenty more years. # masked is a DataFrame where values lower than threshold are NaN, # 0 1 2 3 4 Algorithms to Live By takes you on a journey of eleven ideas from computer science, that we, knowingly or not, use in our lives every day. I really loved how this chapter ended with a discussion on randomness, evolution, and creativity. Algorithms to Live By Optimal Stopping 9 When to Stop Looking 2 Explore/Exploit 31 The Latest vs. the Greatest 3 Sorting 59 Making Order 4 Caching 84 Forget About Jt 5 Scheduling 105 First Things First 6 Bayes's Rule 128 Predicting the Future 7 Overfitting 149 When to Think Less . With sorting, size is a recipe for disaster: perversely, as a sort grows larger, the unit cost of sorting, instead of falling, rises. This “Optimal Stopping” is one of twelve subjects examined in Christian’s (and co-author Tom Griffiths’) book, Algorithms to Live By. Laplace’s law – estimate probability of future event based on previous results. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, # 6 8 89 52 1 83 If you keep constantly thinking about the novel you are about to finish while studying for the exam you need to take, maybe it is better to finish the novel first, unblocking the high priority task at hand. Merrill Flood. None of these tasks had weight (i.e. In Packet Switching, there are no connections. Note how comparison count increases roughly by 4 (6, 30, 132) as the length of the lists increase by 2 (3, 6, 12). Before you get too excited, here’s the sobering bit: this optimal strategy fails 63% of the time. # dtype: float64, # 0 False Sorting five shelves of books will take not five times as long as sorting a single shelf, but twenty-five times as long. Explore/Exploit. Share. Inconsistency in Time Management Best Sellers, “Sorting Out Sorting” – Baecker, Ronald M., with the assistance of David Sherman, The Information: A History, a Theory, a Flood, A Protocol for Packet Network Intercommunication, Sorting Socks and Other Practical Uses of Algorithms - Michiel Stock, Immediately do a task that would take 2 minutes or less, Begin with the most difficult task and move to easier ones, First schedule your social engagements, fill the gaps with work, There is nothing so fatiguing as the eternal hanging on of an uncompleted task, Deliberately do not do things right away, wait on them. Many problems that we all deal with as part of life have practical solutions that come from computer science, and this book gives a number of examples. (The other subjects are: Explore/Exploit; Sorting; Caching; Scheduling; Bayes’ Rule; Overfitting; Relaxation; Randomness; Networking; Game Theory; and Computational Kindness. // Sort tasks by minimum work needed. # 3 52 1 87 29 37 # There is a better candidate at index 7 with a value of 91! In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Reverend Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. Algorithms to Live By 1 Optimal Stopping When to Stop Looking 2 Explore/Exploit The Latest vs. the Greatest 3 Sorting Making Order 4 Caching Forget About It 5 Scheduling First Things First 6 Bayes’s Rule Context Switching however is expensive, and may end up in asking the question: Now where was I?. # 3 False But if it were a power-law distribution, then he knew the more he lived, the more likely he would live even longer. From poker to auctions, especially ad auctions that form the basis of the internet economy today (think Google and Facebook), Game Theory is another field of computer science/math that you cannot miss to explore! Relaxation. For any power-law distribution, Bayes's Rule indicates that the appropriate prediction strategy is a Multiplicative Rule Multiply the quantity observed so far by some constant factor. The Secretary Problem. Context Switching helps us getting things done by pausing at a state of a task, getting other things done, and getting back to it. # However, in this case, we are not actually picking the best candidate we can.. After all, you can make a case that all art stems out of some form of randomness. …and, if you liked the ideas in the Machine Learning part and want to dive deeper, check this one out: Learn Machine Learning | Commonlounge_This 29-part course consists of tutorials on ML concepts and algorithms, as well as end-to-end follow-along ML…_www.commonlounge.com. Sieve of Eratosthenes Implementation in Java, Sieve of Eratosthenes Implementation in Python. The term connection has a wide variety of meanings. So the optimal strategy involves interviewing and rejecting the first few candidates no matter how good they are: just to set up the baseline first and then hiring the best you’ve seen so far after. Sorting algorithms are usually the first ones that any introductory Computer Science course covers. Starting from 38 percentile, hire the first candidate encountered where the candidate is better compared to best observed in first 37 percentile.

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