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Edward Malthouse
เข้าร่วมเมื่อ 31 ต.ค. 2012
Hierarchical clustering
Introduces hierarchical clustering including average, complete and single linkage, and Ward's method.
มุมมอง: 720
วีดีโอ
8 Introduction to Migration model for lifetime value
มุมมอง 3413 ปีที่แล้ว
This shows how to use Markov Chains to estimate customer lifetime value, called the migration model
10 Migration model lifetime value applications part 2
มุมมอง 4323 ปีที่แล้ว
More examples of how to apply the migration (Markov chain) model for customer lifetime value
9 Migration model lifetime value applications part 1
มุมมอง 2393 ปีที่แล้ว
Examples of how to apply the migration model (Markov chain) for estimating customer lifetime value
7 Discrete time survival model for customer lifetime value
มุมมอง 1.4K3 ปีที่แล้ว
I show how to estimate retention rates for customer lifetime value using the discrete time survival model
6 General retention model for lifetime value with stratification in R
มุมมอง 2973 ปีที่แล้ว
I show how to use R to estimate retention rates for lifetime value
3. General retention model (GRM) for lifetime value
มุมมอง 3073 ปีที่แล้ว
This defines the general retention model (GRM) for customer lifetime value (CLV). I show how to compute CLV using Excel if the retention probabilities are provided.
5. Kaplan-Meier estimate of retention rates for general retention model of lifetime value
มุมมอง 4873 ปีที่แล้ว
This covers the Kaplan Meier estimate of survival probabilities and applies them to estimating customer lifetime value.
4. Simple retention model (SRM) for lifetime value in Excel
มุมมอง 2753 ปีที่แล้ว
This shows how to compute the PDF, expected value and lifetime value by "brute force" in Excel, as well as with the formulas derived in my other video.
2. Simple retention model for lifetime value
มุมมอง 3943 ปีที่แล้ว
This covers the simple retention model for estimating customer lifetime value (CLV). I discuss the assumptions, show how to compute the expected time until cancelation and the lifetime value with or without payments at time 0.
1. Intro to customer lifetime value (CLV)
มุมมอง 5313 ปีที่แล้ว
Introduces the concepts of customer profitability, customer lifetime value, prospect lifetime value, customer equity for customer evaluation
5 Picking number of clusters, profiling
มุมมอง 8373 ปีที่แล้ว
This video discusses different ways of selecting the number of clusters including using the objective function value, pseudo F, silhouette statistics and managerial implications. I show how to profile clusters using one-way ANOVA and the chi-square test of independence.
8. Gaussian mixture model part 2
มุมมอง 3403 ปีที่แล้ว
Bivariate and multi-variate Gaussian mixture model in R and python
7. GMM part 1
มุมมอง 3693 ปีที่แล้ว
Introduction to Gaussian mixture models. This covers the basic distributions: class-conditional, prior, posterior, observed. I show to estimate it in R and Python.
2 One way ANOVA review for cluster analyssi
มุมมอง 8573 ปีที่แล้ว
2 One way ANOVA review for cluster analyssi
Introduction to dimension reduction recommender systems
มุมมอง 1373 ปีที่แล้ว
Introduction to dimension reduction recommender systems
5 5 introduction to vector autoregression models
มุมมอง 2903 ปีที่แล้ว
5 5 introduction to vector autoregression models
5 3 Causality and Quasi experimental designs
มุมมอง 1373 ปีที่แล้ว
5 3 Causality and Quasi experimental designs
How did you get the value "1" (the upper bound) for the last two (0ne-Sided) CI's?
Thanks for sharing amazing lectures. I was wondering if you could organize courses as a form of playlist so that audience can follow lecture sequences more smoothly?
Thankyou ♥️
Great lecture, any source on their relation to the encoder-decoder deep learning models?
Understood some lecture slides now
What is the meaning of a customer getting censored?
your lecture is so awesome please upload more videos 🤩😌
awesomee 🤩
awesome🤩
Thank you for such detailed explanation. Gonna explore all of your time series videos now.
This should get more views ! Thanks alot !
Thank you! i was looking for a solution abbout how to calcualte the second moment. you explained well.
YOU'RE THE BEST TEACHER IVE EVER MET THANJ YOU SO MUCH FOR YOUR VIDEO <3
Thank you Sir
This is by far the best explanations for such a topic on youtube. You really have my uttermost respect and gratitude.
great sir , thanks alot
great teaching style, thank you!
Thanks a lot
Incredibly clear. Your interpretation is way better than my professor's! Thank you, sir.
Thank you for all your videos!
>return("thank you professor")
I typed out the function manually if anyone wants it freqdist=function(x, freqorder=F) { Counts=table(x) n=sum(counts) if (freqorder) ord=order(-counts) else ord+1:length(counts) data.frame( row.names=row.name(counts[ord]), Counts=as.vector(counts[ord]), Percent=100*as.vector(counts[ord])/n, CumCount=cumsum(as.vector(counts[ord])), CumpPercent=100*cumsum(as.vector(counts[ord]))/n ) } Some formatting will of course be needed.
Can you provide the link for this amazon data?
This series of videos are really great! Thank you so much. Wondering is it possible to upload the video notes here?
I love u
perhaps women are richer than men
@12:00 so just because the y-distance from the intersection point to x2 is longer, the probability that it came from class 2 is higher ? I see that Posterior is direct proportional to conditional to class conditional distribution times prior, so it would also depend on prior, am I wrong ?
How can you do misclassification in mclust when there is a noise ?
What kind of script is that ?
@1:08 when you refer to "last week" which video is that ? There's no playlist for this subject. Your lectures are very good but in a random order
Thanks!!
This is what TH-cam should be used for. Very clear and easy to follow. Thank you
Is there any way I could get your pdf that youre presenting off of? Quality material. '
Really like your way of teaching! I learned a lot from watching your series of videos. Thank you!
Excelent video, professor. Really thorough explanation. Thank you for sharing this.
is there any way we can get the PDF copy of these notes?
Very good, will be consuming a lot more videos from your channel. Thanks so much!
🐐🐐
Very helpful! The best video on this topic.
much clear explanation than my prof
thank you :)
Wow! This is fantastic. I want to thank you for helping me with my frequency distribution graph.
this video was actually amazing. I haven't understood stats for the two years of uni, now in my third and watching this video actually clicked everything together for me. I usually don't comment on stuff but just wanted to show my appreciation for your help. thank you so much!
awesome video sir. thank you
Thanks so much for these series of video, It has been so helpful!
Very good interpretation.. indeed
Thanks.. now lot of concepts u made clear.
nice video, but can the frequency of each variable be increase in a frequency table ?
This is the best explanation on all of TH-cam! you bring together the Maths, the intuition, derivations and illustrations. Thank you so much !
great