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302 <li> 303 <a href="#2021"> 304 <strong>2021</strong> <span class="taxonomy__count">7</span> 305 </a> 306 </li> 307 308 <li> 309 <a href="#2020"> 310 <strong>2020</strong> <span class="taxonomy__count">1</span> 311 </a> 312 </li> 313 314</ul> 315 316 317 318 319 <section id="2021" class="taxonomy__section"> 320 <h2 class="archive__subtitle">2021</h2> 321 <div class="entries-list"> 322 323 324 325 326 327<div class="list__item"> 328 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 329 330 <h2 class="archive__item-title no_toc" itemprop="headline"> 331 332 <a href="/posts/few_shot_learning" rel="permalink">Basics of few-shot learning with optimization-based meta-learning 333</a> 334 335 </h2> 336 337 338 <p class="page__meta"> 339 340 341 342 343 344 345 346 347 <span class="page__meta-readtime"> 348 <i class="far fa-clock" aria-hidden="true"></i> 349 350 7 minute read 351 352 </span> 353 354 </p> 355 356 357 <p class="archive__item-excerpt" itemprop="description">Many machine learning models (particularly deep neural nets) require extensive training data. The idea of few-shot learning is to find ways to build models t...</p> 358 </article> 359</div> 360 361 362 363 364 365 366<div class="list__item"> 367 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 368 369 <h2 class="archive__item-title no_toc" itemprop="headline"> 370 371 <a href="/posts/classifier-shapley-interaction" rel="permalink">Understanding binary classifier model structure based on Shapley feature interaction patterns 372</a> 373 374 </h2> 375 376 377 <p class="page__meta"> 378 379 380 381 382 383 384 385 386 <span class="page__meta-readtime"> 387 <i class="far fa-clock" aria-hidden="true"></i> 388 389 17 minute read 390 391 </span> 392 393 </p> 394 395 396 <p class="archive__item-excerpt" itemprop="description">I have written a full version of this article, available on bioRxiv. A copy of which is presented below. 397 398</p> 399 </article> 400</div> 401 402 403 404 405 406 407<div class="list__item"> 408 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 409 410 <h2 class="archive__item-title no_toc" itemprop="headline"> 411 412 <a href="/posts/expectation-maximization" rel="permalink">Expectation-maximization in general and for Gaussian mixtures 413</a> 414 415 </h2> 416 417 418 <p class="page__meta"> 419 420 421 422 423 424 425 426 427 <span class="page__meta-readtime"> 428 <i class="far fa-clock" aria-hidden="true"></i> 429 430 9 minute read 431 432 </span> 433 434 </p> 435 436 437 <p class="archive__item-excerpt" itemprop="description">In statistical inference, we want to find what is the best model parameters given the observed data. In the frequentist view, this is about maximizing the li...</p> 438 </article> 439</div> 440 441 442 443 444 445 446<div class="list__item"> 447 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 448 449 <h2 class="archive__item-title no_toc" itemprop="headline"> 450 451 <a href="/posts/vaccine_efficacy_bayesian" rel="permalink">Vaccine efficacies (with COVID-19 examples) - Bayesian posterior VE/CI calculations 452</a> 453 454 </h2> 455 456 457 <p class="page__meta"> 458 459 460 461 462 463 464 465 466 <span class="page__meta-readtime"> 467 <i class="far fa-clock" aria-hidden="true"></i> 468 469 13 minute read 470 471 </span> 472 473 </p> 474 475 476 <p class="archive__item-excerpt" itemprop="description">It was interesting to see that Pfizer/BioNTech used a Bayesian analysis of the primary endpoint for its COVID-19 vaccine. There are a few posts on this alrea...</p> 477 </article> 478</div> 479 480 481 482 483 484 485<div class="list__item"> 486 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 487 488 <h2 class="archive__item-title no_toc" itemprop="headline"> 489 490 <a href="/posts/mcmc-gibbs-sampling-multivariate" rel="permalink">Gibbs sampling of multivariate probability distributions 491</a> 492 493 </h2> 494 495 496 <p class="page__meta"> 497 498 499 500 501 502 503 504
505 <span class="page__meta-readtime"> 506 <i class="far fa-clock" aria-hidden="true"></i> 507 508 5 minute read 509 510 </span> 511 512 </p> 513 514 515 <p class="archive__item-excerpt" itemprop="description">This is a continuation of a previous article I have written on Bayesian inference using Markov chain Monte Carlo (MCMC). Here we will extend to multivariate ...</p> 516 </article> 517</div> 518 519 520 521 522 523 524<div class="list__item"> 525 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 526 527 <h2 class="archive__item-title no_toc" itemprop="headline"> 528 529 <a href="/posts/mcmc-bayesian-inference" rel="permalink">Bayesian inference using Markov Chain Monte Carlo with Python (from scratch and with PyMC3) 530</a> 531 532 </h2> 533 534 535 <p class="page__meta"> 536 537 538 539 540 541 542 543 544 <span class="page__meta-readtime"> 545 <i class="far fa-clock" aria-hidden="true"></i> 546 547 9 minute read 548 549 </span> 550 551 </p> 552 553 554 <p class="archive__item-excerpt" itemprop="description">A guide to Bayesian inference using Markov Chain Monte Carlo (Metropolis-Hastings algorithm) with python examples, and exploration of different data size/par...</p> 555 </article> 556</div> 557 558 559 560 561 562 563<div class="list__item"> 564 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 565 566 <h2 class="archive__item-title no_toc" itemprop="headline"> 567 568 <a href="/posts/monte-carlo-integration" rel="permalink">Monte Carlo integration in Python over univariate and multivariate functions 569</a> 570 571 </h2> 572 573 574 <p class="page__meta"> 575 576 577 578 579 580 581 582 583 <span class="page__meta-readtime"> 584 <i class="far fa-clock" aria-hidden="true"></i> 585 586 7 minute read 587 588 </span> 589 590 </p> 591 592 593 <p class="archive__item-excerpt" itemprop="description">Monte Carlo integration is a basic Monte Carlo method for numerically estimating 594the integration of a function \(f(x)\). We will discuss here the theory alon...</p> 595 </article> 596</div> 597 598 599 </div> 600 <a href="#page-title" class="back-to-top">Back to top ↑</a> 601 </section> 602 603 <section id="2020" class="taxonomy__section"> 604 <h2 class="archive__subtitle">2020</h2> 605 <div class="entries-list"> 606 607 608 609 610 611<div class="list__item"> 612 <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork"> 613 614 <h2 class="archive__item-title no_toc" itemprop="headline"> 615 616 <a href="/posts/gaussian-process-regression" rel="permalink">Getting started with Gaussian process regression modeling 617</a> 618 619 </h2> 620 621 622 <p class="page__meta"> 623 624 625 626 627 628 629 630 631 <span class="page__meta-readtime"> 632 <i class="far fa-clock" aria-hidden="true"></i> 633 634 5 minute read 635 636 </span> 637 638 </p> 639 640 641 <p class="archive__item-excerpt" itemprop="description">Gaussian processing (GP) is quite a useful technique that enables a 642non-parametric Bayesian approach to modeling. It has wide applicability in areas 643such as ...</p> 644 </article> 645</div> 646 647 648 </div> 649 <a href="#page-title" class="back-to-top">Back to top ↑</a> 650 </section> 651 652 653 </div> 654</div> 655 </div> 656 657 658 659 <div id="footer" class="page__footer"> 660 <footer> 661 <!-- start custom footer snippets --> 662 663<!-- end custom footer snippets --> 664 <div class="page__footer-follow"> 665 <ul class="social-icons"> 666 667 <li><strong>Follow:</strong></li> 668 669 670 671 672 673 674 675 676 677 <li><a href="https://github.com/boyangzhao" rel="nofollow noopener noreferrer"><i class="fab fa-fw fa-github" aria-hidden="true"></i> GitHub</a></li> 678 679 680 681 682 683 684 685 686 687 688 <li><a href="/feed.xml"><i class="fas fa-fw fa-rss-square" aria-hidden="true"></i> Feed</a></li> 689 </ul> 690</div> 691 692<div class="page__footer-copyright">© 2025 Boyang Zhao. Powered by <a href="https://jekyllrb.com" rel="nofollow">Jekyll</a> &
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