A Bayesian would have two initial thoughts 1. Yields could kill the stock market relative to 2008--2025 2. Equities had major bull trends in the 1980s-1990s under the same conditions as present Anybody have thoughts on these two conditions What would be the next major bullet point for a Bayesian?

Visual proof of Bayes' Theorem using areas and shapes. P(A|B) = [P(B|A) × P(A)] / P(B) The overlapping regions illustrate how the conditional probability P(A given B) equals the joint probability divided by the marginal probability of B.

Earlier this weekend I had an X post showing a rising wedge pattern in Copper with waning velocity. Wedges tend to suggest short-to-intermediate trend corrections. Factor LLC, my trading company since 1981, approaches trading opportunities with a Bayesian philosophy. Consistent with this Bayesian mindset we often intentionally try to create binary technical interpretations of the same chart. Our entire approach to trading attempts toward agnosticism on bull/bear narratives. The same price history that contained the wedge also displays clearly a market blow-off of some magnitude based on parabolic principles. It is ALWAYS dangerous trying to pick tops of markets. As a firm trying to pick tops and bottoms is forbidden to traders.

Blake Griffin drives, spins, confuses Cory Joseph, and dunks it over Aron Baynes, and then blocks him at the other end (with replays) Cory Joseph and Boris Diaw with the miscommunication Los Angeles Clippers vs. San Antonio Spurs - Game 1 of the First Round - 2015 NBA Playoffs - 4/19/2015

bro from first principles the overton window has shifted. that's why im building an AGI startup that's basically the lindy effect but for retardmaxxing. it's high signal bro and with the network effects of bayesian analysis this will hyperscale. we even got into yc bro but we dropped out bc of the low signal we were getting from other yc founders here in sf.

THE MATH NEEDED FOR TRADING (COMPLETE ROADMAP): today I'll will break down the essential math you need for trading & this is the exact roadmap that helped me personally when i started, i thought math was for interviews, two months into live trading i realized every position i took was pure math running in production here's the complete map of what math actually fires on real trades: --------------- 1. statistics and probability every price move is signal plus randomness. statistics separates the two what you need: > mean, median, expected value = EV formula (win% × avg win) - (loss% × avg loss) is what you're actually maximizing > variance and standard deviation = foundation of every position sizing formula, becomes volatility when applied to returns > correlation from -1 to +1 = tells you if strategies are actually independent > correlation 0.9 across 3 strategies = you have one strategy dressed as three > conditional probability = the biggest edge upgrade retail misses. P(win) = 55% unconditionally, but 70% when VIX < 15 > Bayes' theorem = how you update beliefs when new information arrives. never work with static beliefs > central limit theorem = why portfolio-level statistics behave cleaner than individual trades > linear and logistic regression = building blocks for mean reversion and binary prediction --------------- 2. linear algebra the moment you hold multiple positions, you're doing linear algebra whether you know it or not what you need: > scalars, vectors, matrices = your portfolio is a weighted sum of vectors > portfolio variance = w^T Σ w. not the sum of individual variances. one matrix operation > eigenvalues and eigenvectors = reveal where risk actually lives. in a 500-stock universe, top 5 eigenvectors explain 70% of variance. the other 495 are noise > PCA and SVD = reduce 50 correlated indicators into 5 independent factors explaining 90% of variation --------------- 3. time series analysis markets have memory. today's price depends on yesterday's. volatility clusters. trends persist what you need: > stationarity = assumption most statistical tests make, but markets aren't stationary, this is why strategies decay when regime shifts > autocorrelation = positive means momentum, negative means mean reversion, zero means random walk > ARIMA = framework for forecasting returns and volatility > GARCH = formalizes what every trader knows, volatility clusters. after a big move expect more volatility > cointegration = the foundation of pairs trading. two assets can both trend but their spread stays stationary --------------- 4. risk management math edge doesn't matter if you size wrong what you need: > Value at Risk = 95% VaR of $5,000 means 95% of the time you won't lose more, but 5% of the time you might lose much more > Sharpe ratio = (return - risk-free rate) / volatility. institutional threshold is Sharpe > 1.5 before deployment > maximum drawdown = biggest peak-to-trough loss. more intuitive than volatility for most traders > Monte Carlo simulation = randomizes trade sequencing to show the range of possible outcomes > Kelly criterion = f* = (bp - q) / b. professionals use 0.25x to 0.5x fractional Kelly because your true edge is never certain --------------- 5. stochastic calculus (for options) if you trade options, every price on your screen came from a stochastic differential equation what you need: > Black-Scholes = dS = μS dt + σS dW. the underlying follows geometric Brownian motion > Ito's Lemma = why the σ² term exists. this is why gamma exists > Heston stochastic volatility = dv = κ(θ - v)dt + ξ√v dW. captures the volatility smile that Black-Scholes misses > delta hedging = stochastic calculus running in production. every rehedge is dictated by the SDE governing the underlying --------------- MINIMUM TO START you don't need everything above to start for your first backtest: > mean, median, standard deviation > correlation > basic probability > Sharpe ratio and max drawdown start with statistics, that alone separates you from 95% of retail traders --------------- every real trade is math executing in production: > entry = conditional probability > validation = statistics > portfolio = linear algebra > sizing = Kelly optimization > risk = VaR, Sharpe, max drawdown > options = stochastic calculus the traders who make consistent money see markets as continuous equations, everyone else guesses if you're a complete beginner shoot me a DM and I'll share the resources with you MATH IS EVERYTHING <3

TIME100 AI 2026 — The 100 Most Influential People in Artificial Intelligence Cover honorees: 🧠 Sam Altman — OpenAI 🧠 Dario Amodei — Anthropic 🧠 Jeff Bezos 🧠 Doreen Bogdan-Martin — Secretary-General, International Telecommunication Union 🧠 Marian Croak 🧠 Larry Ellison — Oracle 🧠 Joseph Gordon-Levitt — actor, filmmaker 🧠 Paris Hilton 🧠 Lila Ibrahim 🧠 Arvind Krishna — IBM 🧠 Fei-Fei Li 🧠 Mira Murati — Thinking Machines Lab 🧠 Elon Musk — xAI 🧠 David Sacks 🧠 Liz Shuler — labor leader 🧠 Ilya Sutskever 🧠 Eddie Wu Also named on the list: 🧠 Mark Chen — OpenAI 🧠 Greg Brockman — OpenAI 🧠 Bernie Sanders — U.S. Senator 🧠 Yang Zhilin — Moonshot AI 🧠 Ben Affleck 🧠 Daniel Nadler — OpenEvidence 🧠 Vivek Raghavan — Sarvam AI 🧠 Utkarsh Saxena — Adalat AI 🧠 Sanjay Mehrotra — Micron Technology 🧠 Anima Anandkumar — Caltech 🧠 Arvind Raman — NIST 🧠 Suchi Saria — Bayesian Health 🧠 Sunny Gandhi — Encode 🧠 Meetali Jain — Tech Justice Law Project 🧠 Ravi Kumar S — Cognizant 🧠 Deng Taihua — AGIBOT 🧠 Alessandra Sala — Shutterstock Notably absent from this year's list: Jensen Huang, Mark Zuckerberg, Sundar Pichai, Satya Nadella, and Demis Hassabis. Published August 27, 2026. TIME's fourth annual list, divided into four categories: Leaders, Innovators, Shapers, and Thinkers. 📌 Source: TIME.