This is just a repost of lbry://@BrendonBrewer/forecasts to the Community Top Bids area. DISCLAIMER: This information is provided for entertainment purposes only. For instance, prior to 2019-04-10, there was a very dumb bug which made the numbers all wrong. Hey, at least *that* bug is gone now. NOTE: If a channel owner routinely withdraws their tips, the numbers here will not reflect those tips! I used [TipPredict](https://github.com/eggplantbren/TipPredict) to forecast LBC tips received over the next month for some publishers on LBRY. This is a CSV file containing a low, a medium, and a high prediction for each of the creators on the list. I made quite basic "status quo" assumptions, which assume that the next month is quite likely to look similar to previous months apart from typical statistical fluctuations. If people find this service useful, I can make the assumptions much more realistic and sophisticated, including things like trends, spikes when you publish, and the fact that what's observed for other channels has some bearing on what you might predict for a particular channel. So far the list of channels I've analysed is quite short (but it's longer than what you see in the thumbnail). If you'd like to be added to the list or removed from it the next time I update this file, contact me on Keybase or the LBRY Discord. Formally, conditional on my assumptions, the probability of the next month's tips being between the low and high forecast is 90%, and it's 50/50 whether it's above or below the medium forecast. Some creators have more uncertain forecasts than others. This is due to two main effects: (i) a shorter tip history, so there's less data to extrapolate from, and/or (ii) greater variation in the size of tips received in the past, which propagates through to greater uncertainty in the size of future tips received.

These are the lectures for the 2017 edition of "STATS 331: Introduction to Bayesian Statistics", taught by Brendon J. Brewer at the University of Auckland. This is an undergraduate course with minimal prerequisites. Some exposure to probability theory, data analysis, and calculus is beneficial. The total running time is 18 hours and 53 minutes, and the file size is 2.57 GiB. The coursebook is at https://www.stat.auckland.ac.nz/~brewer/stats331.pdf. The approximate starting times of the individual lectures are: Lecture 1 - Introduction - 00:00:00 Lecture 2 - Maths, Stats, and R - 00:48:50 Lecture 3 - First Examples - 01:42:00 Lecture 4 - Bigger Bayes Boxes - 02:33:10 Lecture 5 - Parameter Estimation - 03:25:20 (see also https://www.stat.auckland.ac.nz/~brewer/taxis.R) Lecture 6 - More Parameter Estimation - 04:19:40 (see also https://www.stat.auckland.ac.nz/~brewer/volcanoes.R) Lecture 7 - Prediction, Analytic Methods - 05:11:10 Lecture 8 - Analytical Methods, Hypothesis Testing - 05:57:20 Lecture 9 - Model Selection - 06:51:30 Lecture 10 - Summaries and Decisions - 07:44:50 Lecture 11 - Intervals, Motivating MCMC - 08:35:00 Lecture 12 - The Metropolis Algorithm - 09:28:55 Lecture 13 - JAGS - 10:24:30 Lecture 14 - More JAGS - 11:16:00 Lecture 15 - Sports Prediction - 12:08:50 Lecture 16 - Linear Regression - 13:02:05 Lecture 17 - Outliers, Multiple Regression - 13:54:15 Lecture 18 - T-Test Type Models - 14:48:40 Lecture 19 - ANOVA Model, Posterior Predictive Checks - 15:39:15 Lecture 20 - Time Series / AR(1) - 16:31:00 Lecture 21 - Dirichlet / Multinomial - 17:14:55 Lecture 22 - Conclusion - 18:01:00 Tip jars: lbry: bDxomVYFm5CCW6XdopH55jLgzyqW7D1DvS ETH: 0xC5d3838417A96243263fE1FC7092b13c2e226D40 LTC: LYrq48bFxpbC678xjbFVPuzCLRrWErW8uM